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# Title Version
Crosses Crosses test 2.0.0
Intersection Compute intersection. 2.0.0
display Print Cheetah templates as HTML 2.0.0
Gdal_Dem Tools to analyze and visualize DEMs. 1.0.0
OTB.OrthoRectification This application allows ortho-rectifying optical and radar images from supported sensors. 1.0.0
OTB.Despeckle Perform speckle noise reduction on SAR image. 1.0.0
OTB.SampleAugmentation Generates synthetic samples from a sample data file. 1.0.0
OTB.TileFusion Fusion of an image made of several tile files. 1.0.0
OTB.GridBasedImageResampling Resamples an image according to a resampling grid 1.0.0
OTB.DomainTransform Domain Transform application for wavelet and fourier 1.0.0
OTB.LineSegmentDetection Detect line segments in raster 1.0.0
OTB.FusionOfClassifications Fuses several classifications maps of the same image on the basis of class labels. 1.0.0
OTB.KmzExport Export the input image in a KMZ product. 1.0.0
OTB.SARDecompositions From one-band complex images (each one related to an element of the Sinclair matrix), returns the selected decomposition. 1.0.0
OTB.LSMSSmallRegionsMerging This application performs the third (optional) step of the exact Large-Scale Mean-Shift segmentation workflow [1]. 1.0.0
OTB.MultiResolutionPyramid Build a multi-resolution pyramid of the image. 1.0.0
OTB.ComputeConfusionMatrix Computes the confusion matrix of a classification 1.0.0
OTB.DynamicConvert Change the pixel type and rescale the image's dynamic 1.0.0
OTB.BlockMatching Performs block-matching to estimate pixel-wise disparities between two images. 1.0.0
OTB.TrainImagesClassifier Train a classifier from multiple pairs of images and training vector data. 1.0.0
OTB.DisparityMapToElevationMap Projects a disparity map into a regular elevation map. 1.0.0
OTB.SFSTextureExtraction Computes Structural Feature Set textures on every pixel of the input image selected channel 1.0.0
OTB.ConnectedComponentSegmentation Connected component segmentation and object based image filtering of the input image according to user-defined criterions. 1.0.0
OTB.StereoRectificationGridGenerator Generates two deformation fields to resample in epipolar geometry, a pair of stereo images up to the sensor model precision 1.0.0
OTB.VectorDataSetField Set a field in vector data. 1.0.0
OTB.DEMConvert Converts a geo-referenced DEM image into a general raster file compatible with OTB DEM handling. 1.0.0
OTB.SARDeburst This application performs deburst of Sentinel1 IW SLC images by removing redundant lines. 1.0.0
OTB.OSMDownloader Download vector data from OSM and store it to file 1.0.0
OTB.BinaryMorphologicalOperation Performs morphological operations on an input image channel 1.0.0
OTB.BandMath Outputs a monoband image which is the result of a mathematical operation on several multi-band images. 1.0.0
OTB.ConcatenateImages Concatenate a list of images of the same size into a single multi-channel one. 1.0.0
OTB.PredictRegression Performs a prediction of the input image according to a regression model file. 1.0.0
OTB.LSMSSegmentation This application performs the second step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS) [1]. 1.0.0
OTB.Quicklook Generates a subsampled version of an image extract 1.0.0
OTB.RadiometricIndices Compute radiometric indices. 1.0.0
OTB.MorphologicalProfilesAnalysis Performs morphological profiles analysis on an input image channel. 1.0.0
OTB.HaralickTextureExtraction Computes Haralick textural features on the selected channel of the input image 1.0.0
OTB.ExtractROI Extract a ROI defined by the user. 1.0.0
OTB.RefineSensorModel Perform least-square fit of a sensor model to a set of tie points 1.0.0
OTB.MultivariateAlterationDetector Change detection by Multivariate Alteration Detector (MAD) algorithm 1.0.0
OTB.HomologousPointsExtraction Compute homologous points between images using keypoints 1.0.0
OTB.DownloadSRTMTiles Download or list SRTM tiles 1.0.0
OTB.TrainRegression Train a classifier from multiple images to perform regression. 1.0.0
OTB.MorphologicalClassification Performs morphological convex, concave and flat classification on an input image channel 1.0.0
OTB.SplitImage Split a N multiband image into N images. 1.0.0
OTB.OGRLayerClassifier Classify an OGR layer based on a machine learning model and a list of features to consider. 1.0.0
OTB.ImageClassifier Performs a classification of the input image according to a model file. 1.0.0
OTB.VectorDataExtractROI Perform an extract ROI on the input vector data according to the input image extent 1.0.0
OTB.StereoFramework Compute the ground elevation based on one or multiple stereo pair(s) 1.0.0
OTB.ComputePolylineFeatureFromImage This application computes the chosen descriptors for each studied polyline contained in the input VectorData. 1.0.0
OTB.LSMSVectorization This application performs the fourth step of the exact Large-Scale Mean-Shift segmentation workflow [1]. 1.0.0
OTB.SampleExtraction Extracts samples values from an image. 1.0.0
OTB.GenerateRPCSensorModel Generate a RPC sensor model from a list of Ground Control Points. 1.0.0
OTB.Rasterization Rasterize a vector dataset. 1.0.0
OTB.RigidTransformResample Resample an image with a rigid transform 1.0.0
OTB.ComputeModulusAndPhase This application computes the modulus and the phase of a complex SAR image. 1.0.0
OTB.Superimpose Using available image metadata, project one image onto another one 1.0.0
OTB.ConvertSensorToGeoPoint Sensor to geographic coordinates conversion. 1.0.0
OTB.VectorClassifier Performs a classification of the input vector data according to a model file. 1.0.0
OTB.LargeScaleMeanShift Large-scale segmentation using MeanShift 1.0.0
OTB.MeanShiftSmoothing This application smooths an image using the MeanShift algorithm. 1.0.0
OTB.ManageNoData Manage No-Data 1.0.0
OTB.MorphologicalMultiScaleDecomposition Perform a geodesic morphology based image analysis on an input image channel 1.0.0
OTB.Smoothing Apply a smoothing filter to an image 1.0.0
OTB.EdgeExtraction This application computes edge features on every pixel of the input image selected channel 1.0.0
OTB.PolygonClassStatistics Computes statistics on a training polygon set. 1.0.0
OTB.ImageEnvelope Extracts an image envelope. 1.0.0
OTB.SARCalibration Perform radiometric calibration of SAR images. Following sensors are supported: TerraSAR-X, Sentinel1 and Radarsat-2.Both Single Look Complex(SLC) and detected products are supported as input. 1.0.0
OTB.SOMClassification SOM image classification. 1.0.0
OTB.ReadImageInfo Get information about the image 1.0.0
OTB.VectorDataTransform Apply a transform to each vertex of the input VectorData 1.0.0
OTB.ConvertCartoToGeoPoint Convert cartographic coordinates to geographic ones. 1.0.0
OTB.VectorDataDSValidation Vector data validation based on the fusion of features using Dempster-Shafer evidence theory framework. 1.0.0
OTB.Convert Convert an image to a different format, optionally rescaling the data and/or changing the pixel type. 1.0.0
OTB.SARPolarMatrixConvert This applications allows converting classical polarimetric matrices to each other. 1.0.0
OTB.ObtainUTMZoneFromGeoPoint UTM zone determination from a geographic point. 1.0.0
OTB.Pansharpening Perform P+XS pansharpening 1.0.0
OTB.VectorDataReprojection Reproject a vector data using support image projection reference, or a user specified map projection 1.0.0
OTB.ClassificationMapRegularization Filters the input labeled image using Majority Voting in a ball shaped neighbordhood. 1.0.0
OTB.PixelValue Get the value of a pixel. 1.0.0
OTB.GeneratePlyFile Generate a 3D Ply file from a DEM and a color image. 1.0.0
OTB.LocalStatisticExtraction Computes local statistical moments on every pixel in the selected channel of the input image 1.0.0
OTB.TrainVectorClassifier Train a classifier based on labeled geometries and a list of features to consider. 1.0.0
OTB.HooverCompareSegmentation Compare two segmentations with Hoover metrics 1.0.0
OTB.ContrastEnhancement This application is the implementation of the histogram equalization algorithm. It can be used to enhance contrast in an image or to reduce the dynamic of the image without losing too much contrast. It offers several options as a no data value, a contrast limitation factor, a local version of the algorithm and also a mode to equalize the luminance of the image. 1.0.0
OTB.ComputeOGRLayersFeaturesStatistics Compute statistics of the features in a set of OGR Layers 1.0.0
OTB.MultiImageSamplingRate Compute sampling rate for an input set of images. 1.0.0
OTB.FineRegistration Estimate disparity map between two images. 1.0.0
OTB.VertexComponentAnalysis Given a set of mixed spectral vectors, estimatereference substances also known as endmembers using the VertexComponent Analysis algorithm. 1.0.0
OTB.SARPolarSynth Gives, for each pixel, the power that would have been received by a SAR system with a basis different from the classical (H,V) one (polarimetric synthetis). 1.0.0
OTB.HyperspectralUnmixing Estimate abundance maps from an hyperspectral image and a set of endmembers. 1.0.0
OTB.TestApplication This application helps developers to test parameters types 1.0.0
OTB.Segmentation Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. 1.0.0
OTB.DSFuzzyModelEstimation Estimate feature fuzzy model parameters using 2 vector data (ground truth samples and wrong samples). 1.0.0
OTB.CompareImages Estimator between 2 images. 1.0.0
OTB.ColorMapping Maps an input label image to 8-bits RGB using look-up tables. 1.0.0
OTB.Rescale Rescale the image between two given values. 1.0.0
OTB.GrayScaleMorphologicalOperation Performs morphological operations on a grayscale input image 1.0.0
OTB.ConcatenateVectorData Concatenate vector data files 1.0.0
OTB.ComputeImagesStatistics Computes global mean and standard deviation for each band from a set of images and optionally saves the results in an XML file. 1.0.0
OTB.SampleSelection Selects samples from a training vector data set. 1.0.0
OTB.BundleToPerfectSensor Perform P+XS pansharpening 1.0.0
IsValid IsValid test 2.0.0
Ogr2Ogr Converts vector data from one format to another. 1.0.0
Union Compute union. 2.0.0
hellor HelloWorld Service in R 2.0.0
HelloPy Create a welcome message string. 2.0.0
Gdal_Contour Builds vector contour lines from a raster elevation model. 1.0.0
Gdal_Warp GDAL Warp Tool 1.0.0
GdalExtractProfile Extract elevation values along a line. 1.0.0
hellojs HelloWorld Service in JavaScript 2.0.0
Within Within test 2.0.0
Buffer Create a buffer around a polygon. 2.0.0
GetStatus Produce an updated ExecuteResponse document. 1.0.0
SAGA.shapes_transect.0 Transect through polygon shapefile 1.0.0
SAGA.sim_ecosystems_hugget.1 02: Carbon Cycle Simulation for Terrestrial Biomass 1.0.0
SAGA.sim_ecosystems_hugget.2 03: Spatially Distributed Simulation of Soil Nitrogen Dynamics 1.0.0
SAGA.sim_ecosystems_hugget.0 01: A Simple Litter System 1.0.0
SAGA.grid_filter.18 Simple Filter (Restricted to Polygons) 1.0.0
SAGA.grid_filter.10 Mesh Denoise 1.0.0
SAGA.grid_filter.7 DTM Filter (slope-based) 1.0.0
SAGA.grid_filter.12 Geodesic Morphological Reconstruction 1.0.0
SAGA.grid_filter.6 Majority/Minority Filter 1.0.0
SAGA.grid_filter.5 Filter Clumps 1.0.0
SAGA.grid_filter.1 Gaussian Filter 1.0.0
SAGA.grid_filter.3 Multi Direction Lee Filter 1.0.0
SAGA.grid_filter.2 Laplacian Filter 1.0.0
SAGA.grid_filter.16 Wombling (Edge Detection) 1.0.0
SAGA.grid_filter.14 Connectivity Analysis 1.0.0
SAGA.grid_filter.8 Morphological Filter 1.0.0
SAGA.grid_filter.0 Simple Filter 1.0.0
SAGA.grid_filter.11 Resampling Filter 1.0.0
SAGA.grid_filter.4 User Defined Filter 1.0.0
SAGA.grid_filter.9 Rank Filter 1.0.0
SAGA.grid_filter.15 Sieve Classes 1.0.0
SAGA.grid_filter.17 Wombling for Multiple Features (Edge Detection) 1.0.0
SAGA.grid_filter.13 Binary Erosion-Reconstruction 1.0.0
SAGA.table_calculus.18 Aggregate Values by Attributes 1.0.0
SAGA.table_calculus.7 Principal Component Analysis 1.0.0
SAGA.table_calculus.12 Minimum Redundancy Feature Selection 1.0.0
SAGA.table_calculus.6 Cluster Analysis 1.0.0
SAGA.table_calculus.5 Running Average 1.0.0
SAGA.table_calculus.1 Field Calculator 1.0.0
SAGA.table_calculus.2 Field Calculator [Shapes] 1.0.0
SAGA.table_calculus.16 Record Statistics 1.0.0
SAGA.table_calculus.14 Cluster Analysis (Shapes) 1.0.0
SAGA.table_calculus.8 Fill Gaps in Ordered Records 1.0.0
SAGA.table_calculus.11 Find Field of Extreme Value 1.0.0
SAGA.table_calculus.9 Fill Gaps in Records 1.0.0
SAGA.table_calculus.15 Field Statistics 1.0.0
SAGA.table_calculus.17 Record Statistics (Shapes) 1.0.0
SAGA.imagery_classification.6 Confusion Matrix (Polygons / Grid) 1.0.0
SAGA.imagery_classification.5 Supervised Classification for Tables 1.0.0
SAGA.imagery_classification.1 K-Means Clustering for Grids 1.0.0
SAGA.imagery_classification.3 Decision Tree 1.0.0
SAGA.imagery_classification.2 Confusion Matrix (Two Grids) 1.0.0
SAGA.imagery_classification.0 Supervised Classification for Grids 1.0.0
SAGA.imagery_classification.4 Supervised Classification for Shapes 1.0.0
SAGA.garden_fractals.5 Gaussian Landscapes 1.0.0
SAGA.garden_fractals.1 Pythagoras' Tree 1.0.0
SAGA.garden_fractals.3 Fractal Dimension of Grid Surface 1.0.0
SAGA.garden_fractals.0 Bifurcation 1.0.0
SAGA.ta_hydrology.18 Flow Accumulation (Mass-Flux Method) 1.0.0
SAGA.ta_hydrology.10 Cell Balance 1.0.0
SAGA.ta_hydrology.7 Slope Length 1.0.0
SAGA.ta_hydrology.6 Flow Path Length 1.0.0
SAGA.ta_hydrology.20 Topographic Wetness Index (TWI) 1.0.0
SAGA.ta_hydrology.1 Flow Accumulation (Recursive) 1.0.0
SAGA.ta_hydrology.29 Flow Accumulation (Parallelizable) 1.0.0
SAGA.ta_hydrology.26 Slope Limited Flow Accumulation 1.0.0
SAGA.ta_hydrology.21 Stream Power Index 1.0.0
SAGA.ta_hydrology.2 Flow Accumulation (Flow Tracing) 1.0.0
SAGA.ta_hydrology.25 LS-Factor, Field Based 1.0.0
SAGA.ta_hydrology.19 Flow Width and Specific Catchment Area 1.0.0
SAGA.ta_hydrology.27 Maximum Flow Path Length 1.0.0
SAGA.ta_hydrology.16 Lake Flood 1.0.0
SAGA.ta_hydrology.0 Flow Accumulation (Top-Down) 1.0.0
SAGA.ta_hydrology.22 LS Factor 1.0.0
SAGA.ta_hydrology.4 Upslope Area 1.0.0
SAGA.ta_hydrology.24 TCI Low 1.0.0
SAGA.ta_hydrology.15 SAGA Wetness Index 1.0.0
SAGA.ta_hydrology.28 Flow between fields 1.0.0
SAGA.ta_hydrology.13 Edge Contamination 1.0.0
SAGA.ta_hydrology.23 Melton Ruggedness Number 1.0.0
SAGA.sim_rivflow.1 LandFlow Version 1.0 (build 3.5.1b) 1.0.0
SAGA.sim_rivflow.3 RiverGridGeneration 1.0.0
SAGA.sim_rivflow.0 RiverBasin 1.0.0
SAGA.imagery_svm.0 SVM Classification 1.0.0
SAGA.sim_qm_of_esp.1 Fill Sinks (QM of ESP) 1.0.0
SAGA.sim_qm_of_esp.3 Successive Flow Routing 1.0.0
SAGA.sim_qm_of_esp.2 Flow Accumulation (QM of ESP) 1.0.0
SAGA.sim_qm_of_esp.0 Diffusive Hillslope Evolution (FTCS) 1.0.0
SAGA.sim_qm_of_esp.4 Diffusive Hillslope Evolution (ADI) 1.0.0
SAGA.grid_spline.7 Multilevel B-Spline for Categories 1.0.0
SAGA.grid_spline.6 Cubic Spline Approximation 1.0.0
SAGA.grid_spline.5 Multilevel B-Spline from Grid Points 1.0.0
SAGA.grid_spline.1 Thin Plate Spline 1.0.0
SAGA.grid_spline.3 B-Spline Approximation 1.0.0
SAGA.grid_spline.2 Thin Plate Spline (TIN) 1.0.0
SAGA.grid_spline.8 Multilevel B-Spline (3D) 1.0.0
SAGA.grid_spline.4 Multilevel B-Spline 1.0.0
SAGA.table_tools.10 Replace Text 1.0.0
SAGA.table_tools.7 Change Field Type 1.0.0
SAGA.table_tools.6 Change Time Format 1.0.0
SAGA.table_tools.20 Add Indicator Fields for Categories 1.0.0
SAGA.table_tools.5 Change Date Format 1.0.0
SAGA.table_tools.1 Transpose Table 1.0.0
SAGA.table_tools.3 Join Attributes from a Table 1.0.0
SAGA.table_tools.21 Table Field Enumeration (Shapes) 1.0.0
SAGA.table_tools.2 Table Field Enumeration 1.0.0
SAGA.table_tools.25 Formatted Text [Shapes] 1.0.0
SAGA.table_tools.8 Append Fields from another Table 1.0.0
SAGA.table_tools.0 Create New Table 1.0.0
SAGA.table_tools.22 Copy Table 1.0.0
SAGA.table_tools.11 Delete Fields 1.0.0
SAGA.table_tools.4 Join Attributes from a Table (Shapes) 1.0.0
SAGA.table_tools.24 Formatted Text 1.0.0
SAGA.table_tools.9 Change Color Format 1.0.0
SAGA.table_tools.15 Copy Selection 1.0.0
SAGA.table_tools.23 Change Field Name 1.0.0
SAGA.pointcloud_tools.10 Point Cloud Attribute Calculator 1.0.0
SAGA.pointcloud_tools.7 Drop Point Cloud Attributes 1.0.0
SAGA.pointcloud_tools.12 Merge Point Clouds 1.0.0
SAGA.pointcloud_tools.6 Point Cloud Reclassifier / Subset Extractor 1.0.0
SAGA.pointcloud_tools.5 Point Cloud to Shapes 1.0.0
SAGA.pointcloud_tools.3 Point Cloud from Shapes 1.0.0
SAGA.pointcloud_tools.2 Point Cloud from Grid Points 1.0.0
SAGA.pointcloud_tools.14 Select Point Cloud from List 1.0.0
SAGA.pointcloud_tools.8 Transform Point Cloud 1.0.0
SAGA.pointcloud_tools.0 Point Cloud Cutter 1.0.0
SAGA.pointcloud_tools.11 Cluster Analysis for Point Clouds 1.0.0
SAGA.pointcloud_tools.4 Point Cloud to Grid 1.0.0
SAGA.pointcloud_tools.9 Point Cloud Thinning (Simple) 1.0.0
SAGA.pointcloud_tools.13 Point Cloud from Table 1.0.0
SAGA.imagery_segmentation.1 Grid Skeletonization 1.0.0
SAGA.imagery_segmentation.3 Seeded Region Growing 1.0.0
SAGA.imagery_segmentation.2 Seed Generation 1.0.0
SAGA.imagery_segmentation.0 Watershed Segmentation 1.0.0
SAGA.imagery_segmentation.4 Superpixel Segmentation 1.0.0
SAGA.ta_morphometry.18 Topographic Position Index (TPI) 1.0.0
SAGA.ta_morphometry.10 Mass Balance Index 1.0.0
SAGA.ta_morphometry.7 Morphometric Protection Index 1.0.0
SAGA.ta_morphometry.12 Diurnal Anisotropic Heat 1.0.0
SAGA.ta_morphometry.6 Real Surface Area 1.0.0
SAGA.ta_morphometry.20 Terrain Surface Texture 1.0.0
SAGA.ta_morphometry.5 Hypsometry 1.0.0
SAGA.ta_morphometry.1 Convergence Index 1.0.0
SAGA.ta_morphometry.3 Surface Specific Points 1.0.0
SAGA.ta_morphometry.26 Upslope and Downslope Curvature 1.0.0
SAGA.ta_morphometry.21 Terrain Surface Convexity 1.0.0
SAGA.ta_morphometry.2 Convergence Index (Search Radius) 1.0.0
SAGA.ta_morphometry.25 Fuzzy Landform Element Classification 1.0.0
SAGA.ta_morphometry.19 TPI Based Landform Classification 1.0.0
SAGA.ta_morphometry.27 Wind Exposition Index 1.0.0
SAGA.ta_morphometry.16 Terrain Ruggedness Index (TRI) 1.0.0
SAGA.ta_morphometry.14 Relative Heights and Slope Positions 1.0.0
SAGA.ta_morphometry.8 Multiresolution Index of Valley Bottom Flatness (MRVBF) 1.0.0
SAGA.ta_morphometry.0 Slope, Aspect, Curvature 1.0.0
SAGA.ta_morphometry.22 Terrain Surface Classification (Iwahashi and Pike) 1.0.0
SAGA.ta_morphometry.11 Effective Air Flow Heights 1.0.0
SAGA.ta_morphometry.4 Curvature Classification 1.0.0
SAGA.ta_morphometry.24 Valley and Ridge Detection (Top Hat Approach) 1.0.0
SAGA.ta_morphometry.9 Downslope Distance Gradient 1.0.0
SAGA.ta_morphometry.15 Wind Effect (Windward / Leeward Index) 1.0.0
SAGA.ta_morphometry.28 Multi-Scale Topographic Position Index (TPI) 1.0.0
SAGA.ta_morphometry.17 Vector Ruggedness Measure (VRM) 1.0.0
SAGA.ta_morphometry.13 Land Surface Temperature 1.0.0
SAGA.ta_morphometry.23 Morphometric Features 1.0.0
SAGA.grid_tools.18 Invert Data/No-Data 1.0.0
SAGA.grid_tools.10 Grid Proximity Buffer 1.0.0
SAGA.grid_tools.7 Close Gaps 1.0.0
SAGA.grid_tools.12 Change Grid Values 1.0.0
SAGA.grid_tools.6 Close One Cell Gaps 1.0.0
SAGA.grid_tools.20 Combine Grids 1.0.0
SAGA.grid_tools.5 Patching 1.0.0
SAGA.grid_tools.1 Aggregate 1.0.0
SAGA.grid_tools.29 Close Gaps with Stepwise Resampling 1.0.0
SAGA.grid_tools.3 Mosaicking 1.0.0
SAGA.grid_tools.26 Proximity Grid 1.0.0
SAGA.grid_tools.21 Grid Cell Index 1.0.0
SAGA.grid_tools.25 Close Gaps with Spline 1.0.0
SAGA.grid_tools.27 Tiling 1.0.0
SAGA.grid_tools.31 Clip Grids 1.0.0
SAGA.grid_tools.8 Grid Buffer 1.0.0
SAGA.grid_tools.33 Copy Grid 1.0.0
SAGA.grid_tools.38 Mosaicking (Grid Collections) 1.0.0
SAGA.grid_tools.0 Resampling 1.0.0
SAGA.grid_tools.22 Grids from classified grid and table 1.0.0
SAGA.grid_tools.35 Mirror Grid 1.0.0
SAGA.grid_tools.11 Change Data Storage 1.0.0
SAGA.grid_tools.37 Combine Classes 1.0.0
SAGA.grid_tools.4 Constant Grid 1.0.0
SAGA.grid_tools.24 Grid Masking 1.0.0
SAGA.grid_tools.32 Select Grid from List 1.0.0
SAGA.grid_tools.30 Transpose Grids 1.0.0
SAGA.grid_tools.9 Threshold Buffer 1.0.0
SAGA.grid_tools.15 Reclassify Grid Values 1.0.0
SAGA.grid_tools.34 Invert Grid 1.0.0
SAGA.grid_tools.28 Shrink and Expand 1.0.0
SAGA.grid_tools.17 Crop to Data 1.0.0
SAGA.grid_tools.39 Change Grid Values - Flood Fill 1.0.0
SAGA.grid_tools.23 Create Grid System 1.0.0
SAGA.contrib_perego.7 Directional Average 1.0.0
SAGA.contrib_perego.6 Destriping with Mask 1.0.0
SAGA.contrib_perego.5 Destriping 1.0.0
SAGA.contrib_perego.1 Average With Thereshold 2 1.0.0
SAGA.contrib_perego.3 Average With Mask 1 1.0.0
SAGA.contrib_perego.2 Average With Thereshold 3 1.0.0
SAGA.contrib_perego.0 Average With Thereshold 1 1.0.0
SAGA.contrib_perego.4 Average With Mask 2 1.0.0
SAGA.ta_channels.7 Valley Depth 1.0.0
SAGA.ta_channels.6 Strahler Order 1.0.0
SAGA.ta_channels.5 Channel Network and Drainage Basins 1.0.0
SAGA.ta_channels.1 Watershed Basins 1.0.0
SAGA.ta_channels.3 Vertical Distance to Channel Network 1.0.0
SAGA.ta_channels.2 Watershed Basins (Extended) 1.0.0
SAGA.ta_channels.0 Channel Network 1.0.0
SAGA.ta_channels.4 Overland Flow Distance to Channel Network 1.0.0
SAGA.ta_compound.0 Basic Terrain Analysis 1.0.0
SAGA.shapes_tools.18 Shapes Buffer 1.0.0
SAGA.shapes_tools.10 Transform Shapes 1.0.0
SAGA.shapes_tools.12 Create Graticule 1.0.0
SAGA.shapes_tools.6 Copy Selection to New Shapes Layer 1.0.0
SAGA.shapes_tools.20 QuadTree Structure to Shapes 1.0.0
SAGA.shapes_tools.29 Focal Mechanism (Beachball Plots) 1.0.0
SAGA.shapes_tools.26 Select Shapes from List 1.0.0
SAGA.shapes_tools.21 Polar to Cartesian Coordinates 1.0.0
SAGA.shapes_tools.2 Merge Layers 1.0.0
SAGA.shapes_tools.25 Land Use Scenario Generator 1.0.0
SAGA.shapes_tools.19 Get Shapes Extents 1.0.0
SAGA.shapes_tools.16 Split Shapes Layer Randomly 1.0.0
SAGA.shapes_tools.0 Create New Shapes Layer 1.0.0
SAGA.shapes_tools.22 Generate Shapes 1.0.0
SAGA.shapes_tools.24 Merge Tables 1.0.0
SAGA.shapes_tools.9 Split Shapes Layer Completely 1.0.0
SAGA.shapes_tools.15 Split Shapes Layer 1.0.0
SAGA.shapes_tools.28 Copy Shapes 1.0.0
SAGA.shapes_tools.17 Split Table/Shapes by Attribute 1.0.0
SAGA.shapes_tools.13 Copy Shapes from Region 1.0.0
SAGA.shapes_tools.23 Convert Vertex Type (2D/3D) 1.0.0
SAGA.db_pgsql.10 List Tables 1.0.0
SAGA.db_pgsql.12 Import Table 1.0.0
SAGA.db_pgsql.6 Execute SQL 1.0.0
SAGA.db_pgsql.20 Import Shapes from PostGIS 1.0.0
SAGA.db_pgsql.16 Import Table from SQL Query (GUI) 1.0.0
SAGA.db_pgsql.33 Import Single Raster Band from PostGIS 1.0.0
SAGA.db_pgsql.0 List PostgreSQL Connections 1.0.0
SAGA.db_pgsql.11 List Table Fields 1.0.0
SAGA.db_pgsql.30 Import Raster from PostGIS 1.0.0
SAGA.db_pgsql.15 Import Table from SQL Query 1.0.0
SAGA.db_pgsql.23 Import Shapes with Joined Data from PostGIS (GUI) 1.0.0
SAGA.imagery_tools.10 Landsat Import with Options 1.0.0
SAGA.imagery_tools.7 Principal Component Based Image Sharpening 1.0.0
SAGA.imagery_tools.12 Local Statistical Measures 1.0.0
SAGA.imagery_tools.6 Colour Normalized Spectral Sharpening 1.0.0
SAGA.imagery_tools.5 Colour Normalized Brovey Sharpening 1.0.0
SAGA.imagery_tools.1 Vegetation Index (Slope Based) 1.0.0
SAGA.imagery_tools.3 Tasseled Cap Transformation 1.0.0
SAGA.imagery_tools.2 Enhanced Vegetation Index 1.0.0
SAGA.imagery_tools.14 Import Landsat Scene 1.0.0
SAGA.imagery_tools.8 Top of Atmosphere Reflectance 1.0.0
SAGA.imagery_tools.0 Vegetation Index (Distance Based) 1.0.0
SAGA.imagery_tools.11 Textural Features 1.0.0
SAGA.imagery_tools.4 IHS Sharpening 1.0.0
SAGA.imagery_tools.9 Automated Cloud Cover Assessment 1.0.0
SAGA.imagery_tools.13 Universal Image Quality Index 1.0.0
SAGA.docs_pdf.1 Shapes Summary Report 1.0.0
SAGA.docs_pdf.2 Terrain Path Cross Sections 1.0.0
SAGA.sim_ihacres.1 IHACRES Version 1.0 1.0.0
SAGA.sim_ihacres.3 IHACRES Elevation Bands 1.0.0
SAGA.sim_ihacres.2 IHACRES Basin 1.0.0
SAGA.sim_ihacres.0 IHACRES Calibration (2) 1.0.0
SAGA.sim_ihacres.4 IHACRES Elevation Bands Calibration 1.0.0
SAGA.imagery_photogrammetry.1 Colorisation (PC) 1.0.0
SAGA.statistics_kriging.1 Simple Kriging 1.0.0
SAGA.statistics_kriging.3 Regression Kriging 1.0.0
SAGA.statistics_kriging.2 Universal Kriging 1.0.0
SAGA.statistics_kriging.0 Ordinary Kriging 1.0.0
SAGA.statistics_kriging.4 Variogram (Dialog) 1.0.0
SAGA.shapes_grid.18 Grid Classes Area for Polygons 1.0.0
SAGA.shapes_grid.10 Grid System Extent 1.0.0
SAGA.shapes_grid.7 Clip Grid with Polygon 1.0.0
SAGA.shapes_grid.6 Vectorising Grid Classes 1.0.0
SAGA.shapes_grid.5 Contour Lines from Grid 1.0.0
SAGA.shapes_grid.1 Add Grid Values to Shapes 1.0.0
SAGA.shapes_grid.3 Grid Values to Points 1.0.0
SAGA.shapes_grid.2 Grid Statistics for Polygons 1.0.0
SAGA.shapes_grid.16 Gradient Vectors from Direction and Length 1.0.0
SAGA.shapes_grid.8 Grid Statistics for Points 1.0.0
SAGA.shapes_grid.0 Add Grid Values to Points 1.0.0
SAGA.shapes_grid.11 Clip Grid with Rectangle 1.0.0
SAGA.shapes_grid.4 Grid Values to Points (randomly) 1.0.0
SAGA.shapes_grid.9 Local Minima and Maxima 1.0.0
SAGA.shapes_grid.15 Gradient Vectors from Surface 1.0.0
SAGA.shapes_grid.17 Gradient Vectors from Directional Components 1.0.0
SAGA.sim_cellular_automata.1 Wa-Tor 1.0.0
SAGA.sim_cellular_automata.0 Conway's Game of Life 1.0.0
SAGA.pj_proj4.20 Geographic Distances 1.0.0
SAGA.pj_proj4.1 Coordinate Transformation (Shapes List) 1.0.0
SAGA.pj_proj4.3 Coordinate Transformation (Grid List) 1.0.0
SAGA.pj_proj4.26 UTM Projection (Shapes) 1.0.0
SAGA.pj_proj4.21 Geographic Distances (Pair of Coordinates) 1.0.0
SAGA.pj_proj4.2 Coordinate Transformation (Shapes) 1.0.0
SAGA.pj_proj4.25 UTM Projection (Shapes List) 1.0.0
SAGA.pj_proj4.16 Tissot's Indicatrix 1.0.0
SAGA.pj_proj4.14 Latitude/Longitude Graticule 1.0.0
SAGA.pj_proj4.4 Coordinate Transformation (Grid) 1.0.0
SAGA.pj_proj4.24 UTM Projection (Grid) 1.0.0
SAGA.pj_proj4.17 Geographic Coordinate Grids 1.0.0
SAGA.pj_proj4.13 Change Longitudinal Range for Grids 1.0.0
SAGA.pj_proj4.23 UTM Projection (Grid List) 1.0.0
SAGA.sim_erosion.0 MMF-SAGA Soil Erosion Model 1.0.0
SAGA.imagery_maxent.1 Maximum Entropy Presence Prediction 1.0.0
SAGA.imagery_maxent.0 Maximum Entropy Classifcation 1.0.0
SAGA.grid_calculus_bsl.1 BSL from File 1.0.0
SAGA.grid_calculus_bsl.0 BSL 1.0.0
SAGA.grid_visualisation.7 Aspect-Slope Grid 1.0.0
SAGA.grid_visualisation.6 Histogram Surface 1.0.0
SAGA.grid_visualisation.5 Color Triangle Composite 1.0.0
SAGA.grid_visualisation.1 Grid Animation 1.0.0
SAGA.grid_visualisation.3 RGB Composite 1.0.0
SAGA.grid_visualisation.8 Terrain Map View 1.0.0
SAGA.grid_visualisation.11 Create a Table from Look-up Table 1.0.0
SAGA.grid_visualisation.4 Create 3D Image 1.0.0
SAGA.grid_visualisation.9 Split RGB Composite 1.0.0
SAGA.shapes_polygons.18 Update 1.0.0
SAGA.shapes_polygons.10 Polygon Parts to Separate Polygons 1.0.0
SAGA.shapes_polygons.7 Polygon Shape Indices 1.0.0
SAGA.shapes_polygons.12 Polygon Self-Intersection 1.0.0
SAGA.shapes_polygons.6 Convert Polygon/Line Vertices to Points 1.0.0
SAGA.shapes_polygons.20 Add Point Attributes to Polygons 1.0.0
SAGA.shapes_polygons.5 Polygon Dissolve 1.0.0
SAGA.shapes_polygons.1 Polygon Centroids 1.0.0
SAGA.shapes_polygons.3 Convert Lines to Polygons 1.0.0
SAGA.shapes_polygons.21 Flatten Polygon Layer 1.0.0
SAGA.shapes_polygons.2 Polygon Properties 1.0.0
SAGA.shapes_polygons.19 Identity 1.0.0
SAGA.shapes_polygons.16 Symmetrical Difference 1.0.0
SAGA.shapes_polygons.14 Intersect 1.0.0
SAGA.shapes_polygons.8 Polygon-Line Intersection 1.0.0
SAGA.shapes_polygons.22 Shared Polygon Edges 1.0.0
SAGA.shapes_polygons.11 Polygon Clipping 1.0.0
SAGA.shapes_polygons.4 Point Statistics for Polygons 1.0.0
SAGA.shapes_polygons.9 Polygons to Edges and Nodes 1.0.0
SAGA.shapes_polygons.15 Difference 1.0.0
SAGA.shapes_polygons.17 Union 1.0.0
SAGA.shapes_polygons.23 Polygon Generalization 1.0.0
SAGA.shapes_lines.7 Line Smoothing 1.0.0
SAGA.shapes_lines.6 Split Lines with Lines 1.0.0
SAGA.shapes_lines.5 Line Dissolve 1.0.0
SAGA.shapes_lines.1 Convert Points to Line(s) 1.0.0
SAGA.shapes_lines.3 Line-Polygon Intersection 1.0.0
SAGA.shapes_lines.2 Line Properties 1.0.0
SAGA.shapes_lines.8 Split Lines at Points 1.0.0
SAGA.shapes_lines.0 Convert Polygons to Lines 1.0.0
SAGA.shapes_lines.4 Line Simplification 1.0.0
SAGA.shapes_lines.9 Line Crossings 1.0.0
SAGA.imagery_vigra.10 Random Forest Presence Prediction (ViGrA) 1.0.0
SAGA.imagery_vigra.7 Fourier Transform (Real, ViGrA) 1.0.0
SAGA.imagery_vigra.6 Fourier Transform Inverse (ViGrA) 1.0.0
SAGA.imagery_vigra.5 Fourier Transform (ViGrA) 1.0.0
SAGA.imagery_vigra.1 Edge Detection (ViGrA) 1.0.0
SAGA.imagery_vigra.3 Distance (ViGrA) 1.0.0
SAGA.imagery_vigra.2 Morphological Filter (ViGrA) 1.0.0
SAGA.imagery_vigra.8 Fourier Filter (ViGrA) 1.0.0
SAGA.imagery_vigra.0 Smoothing (ViGrA) 1.0.0
SAGA.imagery_vigra.11 Random Forest Table Classification (ViGrA) 1.0.0
SAGA.imagery_vigra.4 Watershed Segmentation (ViGrA) 1.0.0
SAGA.imagery_vigra.9 Random Forest Classification (ViGrA) 1.0.0
SAGA.pj_georeference.6 World File from Flight and Camera Settings 1.0.0
SAGA.pj_georeference.5 Define Georeference for Grids 1.0.0
SAGA.pj_georeference.2 Warping Shapes 1.0.0
SAGA.pj_georeference.4 Direct Georeferencing of Airborne Photographs 1.0.0
SAGA.statistics_regression.10 Trend Analysis 1.0.0
SAGA.statistics_regression.7 GWR for Multiple Predictors 1.0.0
SAGA.statistics_regression.12 Multiple Linear Regression Analysis 1.0.0
SAGA.statistics_regression.6 GWR for Multiple Predictor Grids 1.0.0
SAGA.statistics_regression.5 GWR for Multiple Predictors (Gridded Model Output) 1.0.0
SAGA.statistics_regression.1 Multiple Regression Analysis (Points and Predictor Grids) 1.0.0
SAGA.statistics_regression.3 GWR for Single Predictor (Gridded Model Output) 1.0.0
SAGA.statistics_regression.2 Polynomial Regression 1.0.0
SAGA.statistics_regression.14 GWR for Grid Downscaling 1.0.0
SAGA.statistics_regression.8 Multiple Regression Analysis (Grid and Predictor Grids) 1.0.0
SAGA.statistics_regression.0 Regression Analysis (Points and Predictor Grid) 1.0.0
SAGA.statistics_regression.11 Trend Analysis (Shapes) 1.0.0
SAGA.statistics_regression.4 GWR for Single Predictor Grid 1.0.0
SAGA.statistics_regression.9 Polynomial Trend from Grids 1.0.0
SAGA.statistics_regression.15 Zonal Multiple Regression Analysis (Points and Predictor Grids) 1.0.0
SAGA.statistics_regression.13 Multiple Linear Regression Analysis (Shapes) 1.0.0
SAGA.grid_gridding.10 Polygon Categories to Grid 1.0.0
SAGA.grid_gridding.7 Angular Distance Weighted 1.0.0
SAGA.grid_gridding.6 Kernel Density Estimation 1.0.0
SAGA.grid_gridding.5 Triangulation 1.0.0
SAGA.grid_gridding.1 Inverse Distance Weighted 1.0.0
SAGA.grid_gridding.3 Natural Neighbour 1.0.0
SAGA.grid_gridding.2 Nearest Neighbour 1.0.0
SAGA.grid_gridding.8 Grid Cell Area Covered by Polygons 1.0.0
SAGA.grid_gridding.0 Shapes to Grid 1.0.0
SAGA.grid_gridding.4 Modifed Quadratic Shepard 1.0.0
SAGA.grid_gridding.9 Polygons to Grid 1.0.0
SAGA.ta_slope_stability.5 ANGMAP 1.0.0
SAGA.ta_slope_stability.1 TOBIA 1.0.0
SAGA.ta_slope_stability.3 WETNESS 1.0.0
SAGA.ta_slope_stability.2 SHALSTAB 1.0.0
SAGA.ta_slope_stability.0 SAFETYFACTOR 1.0.0
SAGA.ta_slope_stability.4 WEDGEFAIL 1.0.0
SAGA.garden_learn_to_program.10 11: Dynamic Simulation - Soil Nitrogen Dynamics 1.0.0
SAGA.garden_learn_to_program.7 08: Extended neighbourhoods - catchment areas (parallel) 1.0.0
SAGA.garden_learn_to_program.12 13: Reprojecting a shapes layer 1.0.0
SAGA.garden_learn_to_program.6 07: Extended neighbourhoods - catchment areas (trace flow) 1.0.0
SAGA.garden_learn_to_program.5 06: Extended neighbourhoods 1.0.0
SAGA.garden_learn_to_program.1 02: Pixel by pixel operations with two grids 1.0.0
SAGA.garden_learn_to_program.3 04: Direct neighbours - more... 1.0.0
SAGA.garden_learn_to_program.2 03: Direct neighbours 1.0.0
SAGA.garden_learn_to_program.8 09: Extended neighbourhoods - catchment areas (recursive) 1.0.0
SAGA.garden_learn_to_program.0 01: My first tool 1.0.0
SAGA.garden_learn_to_program.11 12: First steps with shapes 1.0.0
SAGA.garden_learn_to_program.4 05: Direct neighbours - slope and aspect 1.0.0
SAGA.garden_learn_to_program.9 10: Dynamic Simulation - Life 1.0.0
SAGA.garden_learn_to_program.13 14: Vectorising channel lines 1.0.0
SAGA.ta_profiles.5 Profile from points 1.0.0
SAGA.ta_profiles.3 Cross Profiles 1.0.0
SAGA.ta_profiles.4 Profiles from Lines 1.0.0
SAGA.ta_preprocessor.6 Burn Stream Network into DEM 1.0.0
SAGA.ta_preprocessor.5 Fill Sinks XXL (Wang & Liu) 1.0.0
SAGA.ta_preprocessor.1 Sink Drainage Route Detection 1.0.0
SAGA.ta_preprocessor.3 Fill Sinks (Planchon/Darboux, 2001) 1.0.0
SAGA.ta_preprocessor.2 Sink Removal 1.0.0
SAGA.ta_preprocessor.0 Flat Detection 1.0.0
SAGA.ta_preprocessor.4 Fill Sinks (Wang & Liu) 1.0.0
SAGA.statistics_points.1 Variogram Cloud 1.0.0
SAGA.statistics_points.3 Minimum Distance Analysis 1.0.0
SAGA.statistics_points.2 Variogram Surface 1.0.0
SAGA.statistics_points.0 Variogram 1.0.0
SAGA.statistics_points.4 Spatial Point Pattern Analysis 1.0.0
SAGA.grids_tools.6 Inverse Distance Weighted (3D) 1.0.0
SAGA.grids_tools.5 Nearest Neighbour (3D) 1.0.0
SAGA.grids_tools.1 Extract Grids from a Grid Collection 1.0.0
SAGA.grids_tools.3 Extract a Grid from a Grid Collection 1.0.0
SAGA.grids_tools.0 Create a Grid Collection 1.0.0
SAGA.grids_tools.4 Add a Grid to a Grid Collection 1.0.0
SAGA.climate_tools.18 Growing Degree Days 1.0.0
SAGA.climate_tools.10 Bioclimatic Variables 1.0.0
SAGA.climate_tools.7 Daily to Hourly ETpot 1.0.0
SAGA.climate_tools.20 Soil Water Balance 1.0.0
SAGA.climate_tools.5 Monthly Global by Latitude 1.0.0
SAGA.climate_tools.1 Multi Level to Points Interpolation 1.0.0
SAGA.climate_tools.3 Annual Course of Daily Insolation 1.0.0
SAGA.climate_tools.21 PhenIps (Table) 1.0.0
SAGA.climate_tools.2 Earth's Orbital Parameters 1.0.0
SAGA.climate_tools.19 Climate Classification 1.0.0
SAGA.climate_tools.14 Frost Change Frequency 1.0.0
SAGA.climate_tools.8 ETpot (after Hargreaves, Grid) 1.0.0
SAGA.climate_tools.0 Multi Level to Surface Interpolation 1.0.0
SAGA.climate_tools.22 PhenIps (Grids) 1.0.0
SAGA.climate_tools.11 Tree Growth Season 1.0.0
SAGA.climate_tools.4 Daily Insolation over Latitude 1.0.0
SAGA.climate_tools.9 Sunrise and Sunset 1.0.0
SAGA.climate_tools.15 Thermic Belt Classification 1.0.0
SAGA.climate_tools.17 Snow Cover 1.0.0
SAGA.climate_tools.13 Wind Effect Correction 1.0.0
SAGA.sim_landscape_evolution.0 SaLEM 1.0.0
SAGA.sim_geomorphology.0 Gravitational Process Path Model 1.0.0
SAGA.grid_analysis.18 Accumulation Functions 1.0.0
SAGA.grid_analysis.10 Analytical Hierarchy Process 1.0.0
SAGA.grid_analysis.7 Covered Distance 1.0.0
SAGA.grid_analysis.12 Aggregation Index 1.0.0
SAGA.grid_analysis.6 Change Vector Analysis 1.0.0
SAGA.grid_analysis.20 Soil Texture Classification for Tables 1.0.0
SAGA.grid_analysis.5 Least Cost Paths 1.0.0
SAGA.grid_analysis.21 Diversity of Categories 1.0.0
SAGA.grid_analysis.19 IMCORR - Feature Tracking 1.0.0
SAGA.grid_analysis.16 Fragmentation (Alternative) 1.0.0
SAGA.grid_analysis.14 Soil Texture Classification 1.0.0
SAGA.grid_analysis.8 Pattern Analysis 1.0.0
SAGA.grid_analysis.0 Accumulated Cost 1.0.0
SAGA.grid_analysis.11 Ordered Weighted Averaging 1.0.0
SAGA.grid_analysis.9 Layer of extreme value 1.0.0
SAGA.grid_analysis.15 Fragmentation (Standard) 1.0.0
SAGA.grid_analysis.17 Fragmentation Classes from Density and Connectivity 1.0.0
SAGA.grid_analysis.13 Cross-Classification and Tabulation 1.0.0
SAGA.statistics_grid.18 Evaluate Statistics for Grids 1.0.0
SAGA.statistics_grid.10 Inverse Principal Components Rotation 1.0.0
SAGA.statistics_grid.7 Global Moran's I for Grids 1.0.0
SAGA.statistics_grid.12 Meridional Grid Statistics 1.0.0
SAGA.statistics_grid.6 Directional Statistics for Single Grid 1.0.0
SAGA.statistics_grid.5 Zonal Grid Statistics 1.0.0
SAGA.statistics_grid.1 Residual Analysis (Grid) 1.0.0
SAGA.statistics_grid.3 Radius of Variance (Grid) 1.0.0
SAGA.statistics_grid.2 Representativeness (Grid) 1.0.0
SAGA.statistics_grid.16 Statistics for Grids from Files 1.0.0
SAGA.statistics_grid.14 Categorical Coincidence 1.0.0
SAGA.statistics_grid.8 Principal Component Analysis 1.0.0
SAGA.statistics_grid.0 Fast Representativeness 1.0.0
SAGA.statistics_grid.11 Longitudinal Grid Statistics 1.0.0
SAGA.statistics_grid.4 Statistics for Grids 1.0.0
SAGA.statistics_grid.9 Multi-Band Variation 1.0.0
SAGA.statistics_grid.15 Focal PCA on a Grid 1.0.0
SAGA.statistics_grid.17 Build Statistics for Grids 1.0.0
SAGA.statistics_grid.13 Save Grid Statistics to Table 1.0.0
SAGA.shapes_points.18 Snap Points to Points 1.0.0
SAGA.shapes_points.10 Add Polygon Attributes to Points 1.0.0
SAGA.shapes_points.7 Remove Duplicate Points 1.0.0
SAGA.shapes_points.12 Convex Hull 1.0.0
SAGA.shapes_points.6 Add Coordinates to Points 1.0.0
SAGA.shapes_points.20 Snap Points to Grid 1.0.0
SAGA.shapes_points.5 Convert Lines to Points 1.0.0
SAGA.shapes_points.3 Point Distances 1.0.0
SAGA.shapes_points.21 Create Random Points 1.0.0
SAGA.shapes_points.2 Create Point Grid 1.0.0
SAGA.shapes_points.19 Snap Points to Lines 1.0.0
SAGA.shapes_points.16 Thiessen Polygons 1.0.0
SAGA.shapes_points.14 Points Thinning 1.0.0
SAGA.shapes_points.8 Clip Points with Polygons 1.0.0
SAGA.shapes_points.0 Convert Table to Points 1.0.0
SAGA.shapes_points.11 Points Filter 1.0.0
SAGA.shapes_points.4 Populate Polygons with Points 1.0.0
SAGA.shapes_points.9 Separate points by direction 1.0.0
SAGA.shapes_points.15 Convert Multipoints to Points 1.0.0
SAGA.shapes_points.17 Aggregate Point Observations 1.0.0
SAGA.tin_tools.6 Flow Accumulation (Parallel) 1.0.0
SAGA.tin_tools.5 Flow Accumulation (Trace) 1.0.0
SAGA.tin_tools.1 Grid to TIN (Surface Specific Points) 1.0.0
SAGA.tin_tools.3 TIN to Shapes 1.0.0
SAGA.tin_tools.2 Shapes to TIN 1.0.0
SAGA.tin_tools.0 Grid to TIN 1.0.0
SAGA.tin_tools.4 Gradient 1.0.0
SAGA.ta_lighting.7 Potential Annual Insolation 1.0.0
SAGA.ta_lighting.6 Visibility (points) 1.0.0
SAGA.ta_lighting.5 Topographic Openness 1.0.0
SAGA.ta_lighting.3 Sky View Factor 1.0.0
SAGA.ta_lighting.2 Potential Incoming Solar Radiation 1.0.0
SAGA.ta_lighting.8 Geomorphons 1.0.0
SAGA.ta_lighting.0 Analytical Hillshading 1.0.0
SAGA.ta_lighting.4 Topographic Correction 1.0.0
SAGA.garden_webservices.2 Geocoding 1.0.0
SAGA.garden_webservices.0 Import a Map via Web Map Service (WMS) 1.0.0
SAGA.db_odbc.10 List Tables 1.0.0
SAGA.db_odbc.5 Import Table 1.0.0
SAGA.db_odbc.8 Import Table from SQL Query 1.0.0
SAGA.db_odbc.4 List Table Fields 1.0.0
SAGA.db_odbc.9 List ODBC Servers 1.0.0
SAGA.sim_hydrology.7 Surface, Gradient and Concentration 1.0.0
SAGA.sim_hydrology.6 Concentration 1.0.0
SAGA.sim_hydrology.5 Surface and Gradient 1.0.0
SAGA.sim_hydrology.1 Kinematic Wave Overland Flow 1.0.0
SAGA.sim_hydrology.3 Water Retention Capacity 1.0.0
SAGA.sim_hydrology.2 TOPMODEL 1.0.0
SAGA.sim_hydrology.0 Soil Moisture Content 1.0.0
SAGA.sim_hydrology.4 Diffuse Pollution Risk 1.0.0
SAGA.grid_calculus.18 Grid Division 1.0.0
SAGA.grid_calculus.10 Grid Standardization 1.0.0
SAGA.grid_calculus.7 Random Field 1.0.0
SAGA.grid_calculus.12 Fuzzy Intersection (AND) 1.0.0
SAGA.grid_calculus.6 Random Terrain 1.0.0
SAGA.grid_calculus.20 Grid Collection Calculator 1.0.0
SAGA.grid_calculus.5 Geometric Figures 1.0.0
SAGA.grid_calculus.1 Grid Calculator 1.0.0
SAGA.grid_calculus.3 Grid Difference 1.0.0
SAGA.grid_calculus.21 Histogram Matching 1.0.0
SAGA.grid_calculus.19 Spherical Harmonic Synthesis 1.0.0
SAGA.grid_calculus.16 Gradient Vector from Polar to Cartesian Coordinates 1.0.0
SAGA.grid_calculus.14 Metric Conversions 1.0.0
SAGA.grid_calculus.8 Grids Sum 1.0.0
SAGA.grid_calculus.0 Grid Normalization 1.0.0
SAGA.grid_calculus.11 Fuzzify 1.0.0
SAGA.grid_calculus.4 Function Plotter 1.0.0
SAGA.grid_calculus.9 Grids Product 1.0.0
SAGA.grid_calculus.15 Gradient Vector from Cartesian to Polar Coordinates 1.0.0
SAGA.grid_calculus.17 Fractal Brownian Noise 1.0.0
SAGA.grid_calculus.13 Fuzzy Union (OR) 1.0.0
SAGA.imagery_opencv.10 Random Forest Classification (OpenCV) 1.0.0
SAGA.imagery_opencv.7 Support Vector Machine Classification (OpenCV) 1.0.0
SAGA.imagery_opencv.12 Logistic Regression (OpenCV) 1.0.0
SAGA.imagery_opencv.6 K-Nearest Neighbours Classification (OpenCV) 1.0.0
SAGA.imagery_opencv.5 Normal Bayes Classification (OpenCV) 1.0.0
SAGA.imagery_opencv.1 Fourier Transformation (OpenCV) 1.0.0
SAGA.imagery_opencv.2 Single Value Decomposition (OpenCV) 1.0.0
SAGA.imagery_opencv.8 Decision Tree Classification (OpenCV) 1.0.0
SAGA.imagery_opencv.0 Morphological Filter (OpenCV) 1.0.0
SAGA.imagery_opencv.11 Artificial Neural Network Classification (OpenCV) 1.0.0
SAGA.imagery_opencv.4 Stereo Match (OpenCV) 1.0.0
SAGA.imagery_opencv.9 Boosting Classification (OpenCV) 1.0.0
Centroid Computes the centroid of a polygon. 2.0.0
Disjoint Disjoint 2.0.0
ConvexHull Compute convex hull. 1.0.0
IsSimple IsSimple test 2.0.0
GetArea Compute geometry area. 2.0.0
hellojs1 HelloWorld Service in JavaScript 2.0.0
failR HelloWorld Service in R 2.0.0
SymDifference Compute symmetric difference. 2.0.0
QREncode Encode a string into a QR Code 1.0.0
Delaunay Delaunay Triangulation. 2.0.0
Simplify Simplifies polygons geometries. 2.0.0
Equals Equals 2.0.0
Distance Compute the distance between two geometries 2.0.0
Boundary Computes boundary. 1.0.0
Intersects Intersects test 2.0.0
Contains Contains 2.0.0
Gdal_Grid Computes a regular grid (raster) from scattered data read from a vector data source. 1.0.0
longProcess Demo long process. 1.0.0
Voronoi Voronoi Diagram. 2.0.0
demo Demo long process. 1.0.0
Gdal_Translate Convert raster data from one format to another. 1.0.0
Touches Compute intersection. 2.0.0
Difference Compute difference. 2.0.0
RVoronoi Voronoi Diagram. 2.0.0
echo Echo input 2.0.0
np.saveRepportFile List all key value from a configuration maps. 2.0.0
np.createIndex Create a view on tables created from two different datasources. 2.0.0
np.getIndexQuote Get Index Quote. 1.0.0
np.insert List all key value from a configuration maps. 2.0.0
np.publishIndex List all key value from a configuration maps. 2.0.0
np.publishFullIndex List all key value from a configuration maps. 2.0.0
np.setCurrentIndex List all key value from a configuration maps. 2.0.0
np.getFavoriteMap Add a map to your favorite ones. 1.0.0
np.list List all key value from a configuration maps. 2.0.0
np.getIndexRequest List all key value from a configuration maps. 2.0.0
np.unzip Zip a raster or vector datasource. 1.0.0
np.orderElement List all key value from a configuration maps. 2.0.0
np.getIndex List all key value from a configuration maps. 2.0.0
np.joinIndexTable List all key value from a configuration maps. 2.0.0
np.saveRepportSettings List all key value from a configuration maps. 2.0.0
np.clientInsert List all key value from a configuration maps. 2.0.0
np.clientView List all key value from a configuration maps. 2.0.0
np.deleteElement Delete an element from PGSQL table 2.0.0
np.saveUploadedFile List all key value from a configuration maps. 2.0.0
np.createAgregate List all key value from a configuration maps. 2.0.0
np.getMapRequest List all key value from a configuration maps. 2.0.0
np.getIndexStyle List all key value from a configuration maps. 2.0.0
np.getMapRequest0 List all key value from a configuration maps. 2.0.0
np.recoverFileFromHexInDb List all key value from a configuration maps. 2.0.0
np.insertElement List all key value from a configuration maps. 2.0.0
np.massiveImport List all key value from a configuration maps. 2.0.0
np.csv2ods Convert CSV file into ODS 2.0.0
np.details List all key value from a configuration maps. 2.0.0
np.convertTo Convert vector data from one format to another. 1.0.0
np.join List all key value from a configuration maps. 2.0.0
np.recoverFileFromHex List all key value from a configuration maps. 2.0.0
np.viewRepport Preview Index pdf file as image. 2.0.0
np.saveRepportFile0 List all key value from a configuration maps. 2.0.0
np.saveIndexDisplaySettings List all key value from a configuration maps. 2.0.0
np.refreshIndex Create a view on tables created from two different datasources. 2.0.0
np.orderElement List all key value from a configuration maps. 2.0.0
np.viewStatOnly Preview Index pdf file as image. 2.0.0
np.createTempFile Create a view on tables created from two different datasources. 2.0.0
np.saveIndexTable List all key value from a configuration maps. 2.0.0
np.parseDocAttr List all key value from a configuration maps. 2.0.0
np.clientViewTable List all tablles from PgDatabase. 2.0.0
np.unpublishIndex List all key value from a configuration maps. 2.0.0
np.getBaseLayersForTable Get base layers used by tables. 1.0.0
np.setIndexQuote Set Index Quote. 1.0.0
np.addLayerForIndex List all key value from a configuration maps. 2.0.0
np.insertElem List all key value from a configuration maps. 2.0.0
np.updateElement List all key value from a configuration maps. 2.0.0
np.testQuery List all key value from a configuration maps. 2.0.0
np.setFavoriteMap Add a map to your favorite ones. 1.0.0
np.updateElem List all key value from a configuration maps. 2.0.0
np.exportTableTo Convert vector data from one format to another. 1.0.0
np.getIndexDisplay List all key value from a configuration maps. 2.0.0
np.getIndexDisplayJs List all key value from a configuration maps. 2.0.0
np.previewDoc Preview Index pdf file as image. 2.0.0
np.getIndexValues List all key value from a configuration maps. 2.0.0
np.isFavoriteMap Add a map to your favorite ones. 1.0.0
np.dropTable Create a view on tables created from two different datasources. 2.0.0
np.clientPrint Client Print Service. 2.0.0
np.searchByName Add a map to your favorite ones. 1.0.0
np.publishIndexMap List all key value from a configuration maps. 2.0.0
np.setLastFile List all key value from a configuration maps. 2.0.0
np.clientImportDataset List all key value from a configuration maps. 2.0.0
np.getLastFile List all key value from a configuration maps. 2.0.0
np.dropTempFile Create a view on tables created from two different datasources. 2.0.0
np.clientDelete List all key value from a configuration maps. 2.0.0
wfs-t.Transaction Execute a WFS Transaction request using a MapServer WFS server 2.0.0
raster-tools.tindex Create an index using raster files 1.0.0
raster-tools.translate Convert raster data from one format to another. 1.0.0
raster-tools.Gdal_Dem Tools to analyze and visualize DEMs. 1.0.0
raster-tools.Gdal_Merge GDAL Mozaic Tool 1.0.0
raster-tools.Gdal_Contour Builds vector contour lines from a raster elevation model. 1.0.0
raster-tools.Gdal_Warp GDAL Warp Tool 1.0.0
raster-tools.hsv_merge Convert raster data from one format to another. 1.0.0
raster-tools.GdalExtractProfile Extract elevation values along a line. 1.0.0
raster-tools.Gdal_Grid Computes a regular grid (raster) from scattered data read from a vector data source. 1.0.0
raster-tools.Gdal_Translate Convert raster data from one format to another. 1.0.0
raster-tools.createTindex Create an index using raster files 1.0.0
raster-tools.copyTileIndex Create an index using raster files 1.0.0
GetConf List all key value from a configuration maps. 2.0.0
pp.convert Convert 2.0.0
pp.printMap Print a Map into a PDF file 2.0.0
pp.printMapImage Print a Map into a PDF file 2.0.0
pp.preview Preview a PDF file as image 2.0.0
symbol-tools.getAllSymbolsForTTF List all charcodes in truetype font 2.0.0
symbol-tools.deleteSymbolFromOrig Remove one or more symbols from the list of available symbols to MapServer 2.0.0
symbol-tools.addSymbolToOrig Add one symbol to the list of available symbols to MapServer 2.0.0
symbol-tools.getSymbolChooser4TTF Display all symbols available as HTML form 2.0.0
symbol-tools.getSymbols List all charcodes in truetype font 2.0.0
georeferencer.cropImage Crop an image. 2.0.0
georeferencer.loadGCP Load a Ground Control Points CSV file 2.0.0
georeferencer.saveGeorefProject Save a MapMint Georeferencing Project 2.0.0
georeferencer.dropLayerFile Remove all files related to a MapMint Georeferencing Project except mapfile 2.0.0
georeferencer.georeference Georeference an image. 2.0.0
georeferencer.saveGeoreferencedProject Save a MapMint Georeferencing Project 2.0.0
georeferencer.listGeoreferencedProject List all MapMint GCP CSV files 2.0.0
georeferencer.copyLayerFile Copy a MapMint Georeferencing Project mapfile 2.0.0
georeferencer.saveGCPAsCSV Save a GCPs list in a CSV file 2.0.0
DifferencePy Compute difference. . 2.0.0
modules.AndroidPosition.setLocation Set location in session file 1.0.0
modules.AndroidPosition.getLocation Get location in session file 1.0.0
modules.AndroidPosition.defineLocation Set location in session file 1.0.0
vector-tools-src.cgi-env.mmExtractVectorInfo Get the centroid of a polygon. 2.0.0
vector-tools-src.cgi-env.EnvelopePy Create a buffer around a polygon. 2.0.0
vector-tools-src.cgi-env.Intersection Compute intersection. 2.0.0
vector-tools-src.cgi-env.Append Set geometry to null. 2.0.0
vector-tools-src.cgi-env.mmVectorInfo2Map Get the centroid of a polygon. 2.0.0
vector-tools-src.cgi-env.DifferencePy Compute difference. . 2.0.0
vector-tools-src.cgi-env.access Access features from a feature collection 2.0.0
vector-tools-src.cgi-env.Xml2JSON Convert XML file into json format. 1.0.0
vector-tools-src.cgi-env.Ogrtindex Create a tileindex. 1.0.0
vector-tools-src.cgi-env.UnionOnePy Compute union. 2.0.0
vector-tools-src.cgi-env.UnionPy Compute union. 2.0.0
vector-tools-src.cgi-env.SpatialQuery Display a buffer hole 2.0.0
vector-tools-src.cgi-env.SymDifferencePy Compute symmetric difference. 2.0.0
vector-tools-src.cgi-env.nullGeo Set geometry to null. 2.0.0
vector-tools-src.cgi-env.getFeaturesCopy Create a copy of features. 2.0.0
vector-tools-src.cgi-env.BufferMask Display a buffer hole 2.0.0
vector-tools-src.cgi-env.mmListVectorDir Get the centroid of a polygon. 2.0.0
vector-tools-src.cgi-env.test Compute intersection. 2.0.0
vector-tools-src.cgi-env.BufferPy Create a buffer around a polygon. 2.0.0
vector-tools-src.cgi-env.FusionIntersectsPy Compute intersection. 2.0.0
vector-tools-src.cgi-env.PointOnSurface Get the point on surface of a polygon. 2.0.0
vector-tools-src.cgi-env.CentroidPy Get the centroid of a polygon. 2.0.0
vector-tools-src.cgi-env.ConvexHullPy Compute convex hull. 1.0.0
vector-tools-src.cgi-env.Remove Remove features. 2.0.0
vector-tools-src.cgi-env.IntersectionNoGeo Compute intersection. 2.0.0
vector-tools-src.cgi-env.ExteriorRingPy Create a buffer around a polygon. 2.0.0
vector-tools-src.cgi-env.vectInfo Get the centroid of a polygon. 2.0.0
vector-tools-src.cgi-env.Intersection0 Compute intersection. 2.0.0
vector-tools-src.cgi-env.createGrid Compute a grid of 1ha parcels over a specific extent 1.0.0
vector-tools-src.cgi-env.createTindex Create an index using vector files 1.0.0
vector-tools-src.cgi-env.IntersectionPy Compute intersection. 2.0.0
vector-tools-src.cgi-env.BoundaryPy Compute boundary. 1.0.0
vector-tools-src.cgi-env.BufferPy Create a buffer around a polygon. 2.0.0
symbol_tools.getAllSymbolsForTTF List all charcodes in truetype font 2.0.0
symbol_tools.deleteSymbolFromOrig Remove one or more symbols from the list of available symbols to MapServer 2.0.0
symbol_tools.addSymbolToOrig Add one symbol to the list of available symbols to MapServer 2.0.0
symbol_tools.getSymbolChooser4TTF Display all symbols available as HTML form 2.0.0
symbol_tools.getSymbols List all charcodes in truetype font 2.0.0
distiller.removeDS Save a section. 1.0.0
distiller.list List all key value from a configuration maps. 2.0.0
distiller.saveDataStorePrivileges save datastore privileges. 1.0.0
distiller.isFavSrs Check for favorite SRS 1.0.0
distiller.options List all key value from a configuration maps. 2.0.0
distiller.directories.load List all key value from a configuration maps. 2.0.0
distiller.directories.removeDS Save a section. 1.0.0
distiller.directories.saveDir Save a section. 1.0.0
distiller.directories.display List all key value from a configuration maps. 2.0.0
distiller.directories.list List all key value from a configuration maps. 2.0.0
distiller.directories.displayJSON List all key value from a configuration maps. 2.0.0
distiller.directories.delete Delete a directory datastore. 1.0.0
distiller.directories.listJson List all files with a given extension from a directory. 2.0.0
distiller.directories.cleanup Clean a directory datastore. 1.0.0
distiller.loadMapForDs List all key value from a configuration maps. 2.0.0
distiller.saveFavSrs Save favorite SRS 1.0.0
distiller.wfs.load Load all WMS/WFS Data Stores. 1.0.0
distiller.wfs.delete Delete a WMS/WFS Data Store. 1.0.0
distiller.wfs.save Create or update a WMS/WFS Data Store. 1.0.0
distiller.wfs.test Test connecting to a WMS or a WFS Data Store. 1.0.0
distiller.postgis.load Save a section. 1.0.0
distiller.postgis.display List all key value from a configuration maps. 2.0.0
distiller.postgis.listTablesAndViews List all tablles from PgDatabase. 2.0.0
distiller.postgis.displayJson List all key value from a configuration maps. 2.0.0
distiller.postgis.getTableContent List all tablles from PgDatabase. 2.0.0
distiller.postgis.delete Save a section. 1.0.0
distiller.postgis.save Save a section. 1.0.0
distiller.postgis.listTables List all tablles from PgDatabase. 2.0.0
distiller.postgis.test Save a section. 1.0.0
distiller.postgis.listSchemas List all schemas from PgDatabase. 2.0.0
distiller.postgis.editTuple List all tablles from PgDatabase. 2.0.0
distiller.postgis.deleteTuple List all tablles from PgDatabase. 2.0.0
distiller.postgis.addColumn List all tablles from PgDatabase. 2.0.0
distiller.postgis.getTableDescription List all tablles from PgDatabase. 2.0.0
distiller.displayHTML List all key value from a configuration maps. 2.0.0
distiller.redrawDsList List all key value from a configuration maps. 2.0.0
distiller.mmVectorInfo2MapJs Get the centroid of a polygon. 2.0.0
context.saveContext Save a WMC 2.0.0
context.loadContext load a WMC 2.0.0
mm4me.replaySqliteHistory Convert PostgreSQL database and MapMint settings of the Tables module in SQLite DB for using with MapMint4ME. 1.0.0
mm4me.joinFiles Convert PostgreSQL database and MapMint settings of the Tables module in SQLite DB for using with MapMint4ME. 1.0.0
mm4me.createSqliteDB4ME Convert PostgreSQL database and MapMint settings of the Tables module in SQLite DB for using with MapMint4ME. 1.0.0
classifier.getClassifierImage Get the classifier image. 1.0.0
authenticate.logOut List all key value from a configuration maps. 2.0.0
authenticate.getLostPassword Regenerate password for a user. 2.0.0
authenticate.clogIn Client Login. 2.0.0
authenticate.registerUser Registering new user 2.0.0
authenticate.saveUserPreferences update users infromations 2.0.0
authenticate.logIn List all key value from a configuration maps. 2.0.0
authenticate.clogOut Log out service. 2.0.0
vector-converter-src.cgi-env.saveLayer Convert vector data from one format to another. 1.0.0
vector-converter-src.cgi-env.convert Convert vector data from one format to another. 1.0.0
vector-converter-src.cgi-env.Recode Recode a file. 1.0.0
vector-converter-src.cgi-env.doZip Zip a raster or vector datasource. 1.0.0
vector-converter-src.cgi-env.addFeatureId Convert vector data from one format to another. 1.0.0
vector-converter-src.cgi-env.Ogr2Ogr Converts vector data from one format to another. 1.0.0
vector-converter-src.cgi-env.cleanUp Convert vector data from one format to another. 1.0.0
vector-converter-src.cgi-env.setSRS Convert vector data from one format to another. 1.0.0
vector-converter-src.cgi-env.convertToKML Convert vector data from one format to another. 1.0.0
vector-converter-src.cgi-env.convertTo Convert vector data from one format to another. 1.0.0
vector-converter-src.cgi-env.exportTo Convert vector data from one format to another. 1.0.0
vector-converter-src.cgi-env.moveFile Convert vector data from one format to another. 1.0.0
vector-converter-src.cgi-env.Converter Convert vector data from one format to another. 1.0.0
print.convert Convert 2.0.0
print.printMap Print a Map into a PDF file 2.0.0
print.printMapImage Print a Map into a PDF file 2.0.0
print.preview Preview a PDF file as image 2.0.0
manage-users.AddGroup Add group on database. 2.0.0
manage-users.requestGroup update user on users database. 2.0.0
manage-users.UpdateUser update user on users database. 2.0.0
manage-users.getTableContent update user on users database. 2.0.0
manage-users.GetGroups Get all groups from database. 2.0.0
manage-users.GetUserInfo Get user info from database. 2.0.0
manage-users.GetGroupsUser Get groups for an user from database. 2.0.0
manage-users.GetUsers Get all users from database. 2.0.0
manage-users.GetUsersGroup Get users for a group from database. 2.0.0
manage-users.UpdateGroup update group on groups database. 2.0.0
manage-users.getTableFeatures update user on users database. 2.0.0
manage-users.AddUser Add user on database. 2.0.0
vector_tools.mmExtractVectorInfo Get the centroid of a polygon. 2.0.0
vector_tools.EnvelopePy Create a buffer around a polygon. 2.0.0
vector_tools.Intersection Compute intersection. 2.0.0
vector_tools.Append Set geometry to null. 2.0.0
vector_tools.mmVectorInfo2Map Get the centroid of a polygon. 2.0.0
vector_tools.DifferencePy Compute difference. . 2.0.0
vector_tools.access Access features from a feature collection 2.0.0
vector_tools.Xml2JSON Convert XML file into json format. 1.0.0
vector_tools.Ogrtindex Create a tileindex. 1.0.0
vector_tools.UnionOnePy Compute union. 2.0.0
vector_tools.UnionPy Compute union. 2.0.0
vector_tools.SpatialQuery Display a buffer hole 2.0.0
vector_tools.SymDifferencePy Compute symmetric difference. 2.0.0
vector_tools.nullGeo Set geometry to null. 2.0.0
vector_tools.getFeaturesCopy Create a copy of features. 2.0.0
vector_tools.BufferMask Display a buffer hole 2.0.0
vector_tools.mmListVectorDir Get the centroid of a polygon. 2.0.0
vector_tools.test Compute intersection. 2.0.0
vector_tools.BufferPy Create a buffer around a polygon. 2.0.0
vector_tools.FusionIntersectsPy Compute intersection. 2.0.0
vector_tools.PointOnSurface Get the point on surface of a polygon. 2.0.0
vector_tools.CentroidPy Get the centroid of a polygon. 2.0.0
vector_tools.ConvexHullPy Compute convex hull. 1.0.0
vector_tools.Remove Remove features. 2.0.0
vector_tools.IntersectionNoGeo Compute intersection. 2.0.0
vector_tools.ExteriorRingPy Create a buffer around a polygon. 2.0.0
vector_tools.vectInfo Get the centroid of a polygon. 2.0.0
vector_tools.Intersection0 Compute intersection. 2.0.0
vector_tools.createGrid Compute a grid of 1ha parcels over a specific extent 1.0.0
vector_tools.createTindex Create an index using vector files 1.0.0
vector_tools.IntersectionPy Compute intersection. 2.0.0
vector_tools.BoundaryPy Compute boundary. 1.0.0
vector_tools.BufferPy Create a buffer around a polygon. 2.0.0
vector-tools.mmExtractVectorInfo Get the centroid of a polygon. 2.0.0
vector-tools.EnvelopePy Create a buffer around a polygon. 2.0.0
vector-tools.Intersection Compute intersection. 2.0.0
vector-tools.Append Set geometry to null. 2.0.0
vector-tools.mmVectorInfo2Map Get the centroid of a polygon. 2.0.0
vector-tools.DifferencePy Compute difference. . 2.0.0
vector-tools.access Access features from a feature collection 2.0.0
vector-tools.Xml2JSON Convert XML file into json format. 1.0.0
vector-tools.Ogrtindex Create a tileindex. 1.0.0
vector-tools.UnionOnePy Compute union. 2.0.0
vector-tools.UnionPy Compute union. 2.0.0
vector-tools.SpatialQuery Display a buffer hole 2.0.0
vector-tools.SymDifferencePy Compute symmetric difference. 2.0.0
vector-tools.nullGeo Set geometry to null. 2.0.0
vector-tools.getFeaturesCopy Create a copy of features. 2.0.0
vector-tools.BufferMask Display a buffer hole 2.0.0
vector-tools.mmListVectorDir Get the centroid of a polygon. 2.0.0
vector-tools.test Compute intersection. 2.0.0
vector-tools.BufferPy Create a buffer around a polygon. 2.0.0
vector-tools.FusionIntersectsPy Compute intersection. 2.0.0
vector-tools.PointOnSurface Get the point on surface of a polygon. 2.0.0
vector-tools.CentroidPy Get the centroid of a polygon. 2.0.0
vector-tools.ConvexHullPy Compute convex hull. 1.0.0
vector-tools.Remove Remove features. 2.0.0
vector-tools.IntersectionNoGeo Compute intersection. 2.0.0
vector-tools.ExteriorRingPy Create a buffer around a polygon. 2.0.0
vector-tools.vectInfo Get the centroid of a polygon. 2.0.0
vector-tools.Intersection0 Compute intersection. 2.0.0
vector-tools.createGrid Compute a grid of 1ha parcels over a specific extent 1.0.0
vector-tools.createTindex Create an index using vector files 1.0.0
vector-tools.IntersectionPy Compute intersection. 2.0.0
vector-tools.BoundaryPy Compute boundary. 1.0.0
vector-tools.BufferPy Create a buffer around a polygon. 2.0.0
manage_users.AddGroup Add group on database. 2.0.0
manage_users.requestGroup update user on users database. 2.0.0
manage_users.UpdateUser update user on users database. 2.0.0
manage_users.getTableContent update user on users database. 2.0.0
manage_users.GetGroups Get all groups from database. 2.0.0
manage_users.GetUserInfo Get user info from database. 2.0.0
manage_users.GetGroupsUser Get groups for an user from database. 2.0.0
manage_users.GetUsers Get all users from database. 2.0.0
{"processes": [{"id": "Crosses", "title": "Crosses test", "description": "This service shall return a true value if and only if the geometries g1 and g2 share some but neither is containe in the other, and the dimension of the intersection is less than that of both of the geometries.", "version": "2.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Crosses"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Crosses.html"}]}, {"id": "Intersection", "title": "Compute intersection. ", "description": "This function SHALL return a bag of geometry values representing the Point set intersection of geometry InputEntity1 and geometry InputEntity2.", "version": "2.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Intersection"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Intersection.html"}]}, {"id": "display", "title": "Print Cheetah templates as HTML", "description": "Print Cheetah templates as HTML.", "version": "2.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/display"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/display.html"}]}, {"id": "Gdal_Dem", "title": "Tools to analyze and visualize DEMs.", "description": "http://www.gdal.org/gdaldem.html", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Gdal_Dem"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Gdal_Dem.html"}]}, {"id": "OTB.OrthoRectification", "title": "This application allows ortho-rectifying optical and radar images from supported sensors.", "description": "This application uses inverse sensor modelling combined with a choice of interpolation functions to resample a sensor geometry image into a ground geometry regular grid. The ground geometry regular grid is defined with respect to a map projection (see map parameter). The application offers several modes to estimate the output grid parameters (origin and ground sampling distance), including automatic estimation of image size, ground sampling distance, or both, from image metadata, user-defined ROI corners, or another ortho-image.A digital Elevation Model along with a geoid file can be specified to account for terrain deformations.In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.OrthoRectification"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.OrthoRectification.html"}]}, {"id": "OTB.Despeckle", "title": "Perform speckle noise reduction on SAR image.", "description": "SAR images are affected by speckle noise that inherently exists in and which degrades the image quality. It is caused by the coherent nature of back-scattered waves from multiple distributed targets. It is locally strong and it increases the mean Grey level of a local area. Reducing the speckle noise enhances radiometric resolution but tend to decrease the spatial resolution.Several different methods are used to eliminate speckle noise, based upon different mathematical models of the phenomenon. The application includes four methods: Lee [1], Frost [2], GammaMAP [3] and Kuan [4]. We sum up below the basic principle of this four methods: * Lee : Estimate the signal by mean square error minimization (MMSE) on a sliding window. * Frost : Also derived from the MMSE criteria with a weighted sum of the values within the window. The weighting factors decrease with distance from the pixel of interest. * GammaMAP : Derived under the assumption of the image follows a Gamma distribution. * Kuan : Also derived from the MMSE criteria under the assumption of non stationary mean and variance. It is quite similar to Lee filter in form.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Despeckle"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Despeckle.html"}]}, {"id": "OTB.SampleAugmentation", "title": "Generates synthetic samples from a sample data file.", "description": "The application takes a sample data file as generated by the SampleExtraction application and generates synthetic samples to increase the number of available samples.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SampleAugmentation"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SampleAugmentation.html"}]}, {"id": "OTB.TileFusion", "title": "Fusion of an image made of several tile files.", "description": "Automatically mosaic a set of non overlapping tile files into a single image. Images must have a matching number of bands and they must be listed in lexicographic order.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.TileFusion"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.TileFusion.html"}]}, {"id": "OTB.GridBasedImageResampling", "title": "Resamples an image according to a resampling grid", "description": "This application allows performing image resampling from an input resampling grid.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.GridBasedImageResampling"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.GridBasedImageResampling.html"}]}, {"id": "OTB.DomainTransform", "title": "Domain Transform application for wavelet and fourier", "description": "Domain Transform application for wavelet and fourier", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.DomainTransform"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.DomainTransform.html"}]}, {"id": "OTB.LineSegmentDetection", "title": "Detect line segments in raster", "description": "This application detects locally straight contours in a image. It is based on Burns, Hanson, and Riseman method and use an a contrario validation approach (Desolneux, Moisan, and Morel). The algorithm was published by Rafael Gromponevon Gioi, J\u00e9r\u00e9mie Jakubowicz, Jean-Michel Morel and Gregory Randall. The given approach computes gradient and level lines of the image and detects aligned points in line support region. The application allows exporting the detected lines in a vector data.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.LineSegmentDetection"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.LineSegmentDetection.html"}]}, {"id": "OTB.FusionOfClassifications", "title": "Fuses several classifications maps of the same image on the basis of class labels.", "description": "This application allows you to fuse several classification maps and produces a single more robust classification map. Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels. - MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected. - DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected. This Belief Function is calculated by mean of the Dempster Shafer combination of Masses of Belief, and indicates the belief that each input classification map presents for each label value. Moreover, the Masses of Belief are based on the input confusion matrices of each classification map, either by using the PRECISION or RECALL rates, or the OVERALL ACCURACY, or the KAPPA coefficient. Thus, each input classification map needs to be associated with its corresponding input confusion matrix file for the Dempster Shafer fusion. - Input pixels with the NODATA label are not handled in the fusion of classification maps. Moreover, pixels for which all the input classifiers are set to NODATA keep this value in the output fused image. - In case of number of votes equality, the UNDECIDED label is attributed to the pixel.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.FusionOfClassifications"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.FusionOfClassifications.html"}]}, {"id": "OTB.KmzExport", "title": "Export the input image in a KMZ product.", "description": "This application exports the input image in a kmz product that can be display in the Google Earth software. The user can set the size of the product size, a logo and a legend to the product. Furthemore, to obtain a product that fits the relief, a DEM can be used.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.KmzExport"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.KmzExport.html"}]}, {"id": "OTB.SARDecompositions", "title": "From one-band complex images (each one related to an element of the Sinclair matrix), returns the selected decomposition.", "description": "From one-band complex images (HH, HV, VH, VV), returns the selected decomposition. All the decompositions implemented are intended for the mono-static case (transmitter and receiver are co-located).There are two kinds of decomposition : coherent ones and incoherent ones.In the coherent case, only the Pauli decomposition is available.In the incoherent case, there the decompositions available : Huynen, Barnes, and H-alpha-A.User must provide three one-band complex images HH, HV or VH, and VV (mono-static case ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SARDecompositions"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SARDecompositions.html"}]}, {"id": "OTB.LSMSSmallRegionsMerging", "title": "This application performs the third (optional) step of the exact Large-Scale Mean-Shift segmentation workflow [1].", "description": "Given a segmentation result (can be the out output parameter of the LSMSSegmentation application [2]) and the original image, it will merge segments whose size in pixels is lower than minsize parameter with the adjacent segments with the adjacent segment with closest radiometry and acceptable size.Small segments will be processed by increasing size: first all segments for which area is equal to 1 pixel will be merged with adjacent segments, then all segments of area equal to 2 pixels will be processed, until segments of area minsize. For large images one can use the tilesizex and tilesizey parameters for tile-wise processing, with the guarantees of identical results.The output of this application can be passed to the LSMSVectorization application [3] to complete the LSMS workflow.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.LSMSSmallRegionsMerging"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.LSMSSmallRegionsMerging.html"}]}, {"id": "OTB.MultiResolutionPyramid", "title": "Build a multi-resolution pyramid of the image.", "description": "This application builds a multi-resolution pyramid of the input image. User can specified the number of levels of the pyramid and the subsampling factor. To speed up the process, you can use the fast scheme option", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.MultiResolutionPyramid"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.MultiResolutionPyramid.html"}]}, {"id": "OTB.ComputeConfusionMatrix", "title": "Computes the confusion matrix of a classification", "description": "This application computes the confusion matrix of a classification map relative to a ground truth dataset. This ground truth can be given as a raster or a vector data. Only reference and produced pixels with values different from NoData are handled in the calculation of the confusion matrix. The confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the output file, the reference and produced class labels are ordered according to the rows/columns of the confusion matrix.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ComputeConfusionMatrix"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ComputeConfusionMatrix.html"}]}, {"id": "OTB.DynamicConvert", "title": "Change the pixel type and rescale the image's dynamic", "description": "This application performs an image pixel type conversion (short, ushort, uchar, int, uint, float and double types are handled). The output image is written in the specified format (ie. that corresponds to the given extension). The conversion can include a rescale of the data range, by default it's set between the 2nd to the 98th percentile. The rescale can be linear or log2. The choice of the output channels can be done with the extended filename, but less easy to handle. To do this, a 'channels' parameter allows you to select the desired bands at the output. There are 3 modes, the available choices are: * grayscale : to display mono image as standard color image * rgb : select 3 bands in the input image (multi-bands) * all : keep all bands.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.DynamicConvert"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.DynamicConvert.html"}]}, {"id": "OTB.BlockMatching", "title": "Performs block-matching to estimate pixel-wise disparities between two images.", "description": "This application allows one to performs block-matching to estimate pixel-wise disparities for a pair of images in epipolar geometry.This application is part of the stereovision pipeline. It can be used after having computed epipolar grids (with StereoRectificationGridGenerator) and resampled each input image into epipolar geometry (with GridBasedImageResampling).The application searches locally for the displacement between a reference image and a secondary image. The correspondence is evaluated for each pixel, based on a pair of local neighborhood windows. The displacement evaluated can be 1D (along lines) or 2D. Parameters allow setting the minimum and maximum disparities to search (both for horizontal and vertical directions). A winner-take-all approach is used to select the best match. There are different metrics implemented to evaluate the match between two local windows: * SSD : Sum of Squared Distances * NCC : Normalized Cross-Correlation * Lp : Lp pseudo normOnce the best integer disparity is found, an optional step of sub-pixel disparity estimation can be performed, with various algorithms (triangular interpolation, parabollic interpolation, dichotimic search). As post-processing, there is an optional step of median filtering on the disparities. One can chose input masks (related to the left and right input image) of pixels for which the disparity should be investigated. Additionally, two criteria can be optionally used to disable disparity investigation for some pixel: a no-data value, and a threshold on the local variance. This allows one to speed-up computation by avoiding to investigate disparities that will not be reliable anyway. For efficiency reasons, if the image of optimal metric values is desired, it will be concatenated to the output image (which will then have three bands : horizontal disparity, vertical disparity and metric value). One can split these images afterward.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.BlockMatching"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.BlockMatching.html"}]}, {"id": "OTB.TrainImagesClassifier", "title": "Train a classifier from multiple pairs of images and training vector data.", "description": "This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the \"Class label field\" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. This application is based on LibSVM, OpenCV Machine Learning (2.3.1 and later), and Shark ML. The output of this application is a text model file, whose format corresponds to the ML model type chosen. There is no image nor vector data output.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.TrainImagesClassifier"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.TrainImagesClassifier.html"}]}, {"id": "OTB.DisparityMapToElevationMap", "title": "Projects a disparity map into a regular elevation map.", "description": "This application uses a disparity map computed from a stereo image pair to produce an elevation map on the ground area covered by the stereo pair.This application is part of the stereo reconstruction pipeline. It can be used after having computed the disparity map with BlockMatching.The needed inputs are : the disparity map, the stereo pair (in original geometry) and the epipolar deformation grids. These grids (computed by StereoRectificationGridGenerator) have to contain the transform between the original geometry (stereo pair) and the epipolar geometry (disparity map). The algorithm for each disparity is the following : * skip if position is discarded by the disparity mask * compute left ray : transform the current position from epipolar geometry to left sensor geometry (left rectification grid) * compute right ray : shift the current position with current disparity and transform from epipolar geometry to right sensor (right rectification grid) * estimate best 3D intersection between left and right rays * for the ground cell of the obtained 3D point, keep its elevation if greater than current elevation (keeps the maximum of elevations of all 3D points in each cell)Minimum and maximum elevations settings are here to bound the reconstructed DEM.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.DisparityMapToElevationMap"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.DisparityMapToElevationMap.html"}]}, {"id": "OTB.SFSTextureExtraction", "title": "Computes Structural Feature Set textures on every pixel of the input image selected channel", "description": "Structural Feature Set [1] are based on the histograms of the pixels in multiple directions of the image. The SFSTextureExtraction application computes the 6 following features: SFS'Length, SFS'Width, SFS'PSI, SFS'W-Mean, SFS'Ratio and SFS'SD (Standard Deviation). The texture indices are computed from the neighborhood of each pixel. It is possible to change the length of the calculation line (spatial threshold), as well as the maximum difference between a pixel of the line and the pixel at the center of the neighborhood (spectral threshold) [2].", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SFSTextureExtraction"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SFSTextureExtraction.html"}]}, {"id": "OTB.ConnectedComponentSegmentation", "title": "Connected component segmentation and object based image filtering of the input image according to user-defined criterions.", "description": "This application allows one to perform a masking, connected components segmentation and object based image filtering. First and optionally, a mask can be built based on user-defined criterions to select pixels of the image which will be segmented. Then a connected component segmentation is performed with a user defined criterion to decide whether two neighbouring pixels belong to the same segment or not. After this segmentation step, an object based image filtering is applied using another user-defined criterion reasoning on segment properties, like shape or radiometric attributes. Criterions are mathematical expressions analysed by the MuParser library (http://muparser.sourceforge.net/). For instance, expression \"((b1>80) and intensity>95)\" will merge two neighbouring pixel in a single segment if their intensity is more than 95 and their value in the first image band is more than 80. See parameters documentation for a list of available attributes. The output of the object based image filtering is vectorized and can be written in shapefile or KML format. If the input image is in raw geometry, resulting polygons will be transformed to WGS84 using sensor modelling before writing, to ensure consistency with GIS software. For this purpose, a Digital Elevation Model can be provided to the application. The whole processing is done on a per-tile basis for large images, so this application can handle images of arbitrary size.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ConnectedComponentSegmentation"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ConnectedComponentSegmentation.html"}]}, {"id": "OTB.StereoRectificationGridGenerator", "title": "Generates two deformation fields to resample in epipolar geometry, a pair of stereo images up to the sensor model precision", "description": "This application generates a pair of deformation grid to stereo-rectify a pair of stereo images according to sensor modelling and a mean elevation hypothesis.This application is the first part of the stereo reconstruction framework. The output deformation grids can be passed to the GridBasedImageResampling application for actual resampling into epipolar geometry.There are several ways to set the elevation source: * An arbitrary constant elevation * A DEM directory * Compute an average elevation from a DEMIf needed, the application can compute inverse resampling grids (from epipolar to original sensor geometry). Don't forget to check the other outputs from the application. For instance, the application gives the X and Y size of the rectified images, along with an estimated baseline ratio.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.StereoRectificationGridGenerator"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.StereoRectificationGridGenerator.html"}]}, {"id": "OTB.VectorDataSetField", "title": "Set a field in vector data.", "description": "Set a specified field to a specified value on all features of a vector data.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.VectorDataSetField"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.VectorDataSetField.html"}]}, {"id": "OTB.DEMConvert", "title": "Converts a geo-referenced DEM image into a general raster file compatible with OTB DEM handling.", "description": "In order to be understood by the Orfeo ToolBox and the underlying OSSIM library, a geo-referenced Digital Elevation Model image can be converted into a general raster image, which consists in 3 files with the following extensions: .ras, .geom and .omd. Once converted, you have to place these files in a separate directory, and you can then use this directory to set the \"DEM Directory\" parameter of a DEM based OTB application or filter.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.DEMConvert"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.DEMConvert.html"}]}, {"id": "OTB.SARDeburst", "title": "This application performs deburst of Sentinel1 IW SLC images by removing redundant lines.", "description": "Sentinel1 IW SLC products are composed of several burst overlapping in azimuth time for each subswath, separated by black lines [1]. The deburst operation consist in generating a continuous image in terms of azimuth time, by removing black separation lines as well as redundant lines between bursts.Note that the output sensor model is updated accordingly. This deburst operation is the perfect preprocessing step to orthorectify S1 IW SLC product with OTB [2] without suffering from artifacts caused by bursts separation.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SARDeburst"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SARDeburst.html"}]}, {"id": "OTB.OSMDownloader", "title": "Download vector data from OSM and store it to file", "description": "The application connects to Open Street Map server, downloads the data corresponding to the spatial extent of the support image, and filters the geometries based on OSM tags to produce a vector data file.This application can be used to download reference data to perform the training of a machine learning model (see for instance [1]).By default, the entire layer is downloaded. The application has a special mode to provide the list of available classes in the layers. The downloaded features are filtered by giving an OSM tag 'key'. In addition, the user can also choose what 'value' this key should have. More information about the OSM project at [2].", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.OSMDownloader"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.OSMDownloader.html"}]}, {"id": "OTB.BinaryMorphologicalOperation", "title": "Performs morphological operations on an input image channel", "description": "This application performs binary morphological operations on a mono band image or a channel of the input.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.BinaryMorphologicalOperation"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.BinaryMorphologicalOperation.html"}]}, {"id": "OTB.BandMath", "title": "Outputs a monoband image which is the result of a mathematical operation on several multi-band images.", "description": "This application performs a mathematical operation on several multi-band images and outputs the result into a monoband image. The given expression is computed at each pixel position. Evaluation of the mathematical formula is done by the muParser libraries.The formula can be written using: * numerical values ( 2.3, -5, 3.1e4, ...) * variables containing pixel values (e.g. : 'im2b3' is the pixel value in 2nd image, 3rd band) * binary operators: * '+' addition, '-' subtraction, '*' multiplication, '/' division * '^' raise x to the power of y * '", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.BandMath"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.BandMath.html"}]}, {"id": "OTB.ConcatenateImages", "title": "Concatenate a list of images of the same size into a single multi-channel one.", "description": "This application performs images channels concatenation. It reads the input image list (single or multi-channel) and generates a single multi-channel image. The channel order is the same as the list.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ConcatenateImages"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ConcatenateImages.html"}]}, {"id": "OTB.PredictRegression", "title": "Performs a prediction of the input image according to a regression model file.", "description": "This application predict output values from an input image, based on a regression model file produced by the TrainRegression application. Pixels of the output image will contain the predicted values fromthe regression model (single band). The input pixels can be optionally centered and reduced according to the statistics file produced by the ComputeImagesStatistics application. An optional input mask can be provided, in which case only input image pixels whose corresponding mask value is greater than 0 will be processed. The remaining of pixels will be given the value 0 in the output image.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.PredictRegression"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.PredictRegression.html"}]}, {"id": "OTB.LSMSSegmentation", "title": "This application performs the second step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS) [1].", "description": "This application will produce a labeled image where neighbor pixels whose range distance is below range radius (and optionally spatial distance below spatial radius) will be grouped together into the same cluster. For large images one can use the tilesizex and tilesizey parameters for tile-wise processing, with the guarantees of identical results.Filtered range image and spatial image should be created with the MeanShiftSmoothing application outputs (fout and foutpos) [2], with modesearch parameter disabled. If spatial image is not set, the application will only process the range image and spatial radius parameter will not be taken into account.Please note that this application will generate a lot of temporary files (as many as the number of tiles), and will therefore require twice the size of the final result in term of disk space. The cleanup option (activated by default) allows removing all temporary file as soon as they are not needed anymore (if cleanup is activated, tmpdir set and tmpdir does not exists before running the application, it will be removed as well during cleanup). The tmpdir option allows defining a directory where to write the temporary files.Please also note that the output image type should be set to uint32 to ensure that there are enough labels available.The output of this application can be passed to the LSMSSmallRegionMerging [3] or LSMSVectorization [4] applications to complete the LSMS workflow.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.LSMSSegmentation"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.LSMSSegmentation.html"}]}, {"id": "OTB.Quicklook", "title": "Generates a subsampled version of an image extract", "description": "Generates a subsampled version of an extract of an image defined by ROIStart and ROISize. This extract is subsampled using the ratio OR the output image Size.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Quicklook"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Quicklook.html"}]}, {"id": "OTB.RadiometricIndices", "title": "Compute radiometric indices.", "description": "This application computes radiometric indices using the relevant channels of the input image. The output is a multi band image into which each channel is one of the selected indices.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.RadiometricIndices"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.RadiometricIndices.html"}]}, {"id": "OTB.MorphologicalProfilesAnalysis", "title": "Performs morphological profiles analysis on an input image channel.", "description": "This algorithm is derived from the following publication:Martino Pesaresi and Jon Alti Benediktsson, Member, IEEE: A new approachfor the morphological segmentation of high resolution satellite imagery.IEEE Transactions on geoscience and remote sensing, vol. 39, NO. 2,February 2001, p. 309-320.Depending of the profile selection, the application provides::- The multi scale geodesic morphological opening or closing profile of the input image.- The multi scale derivative of the opening or closing profile.- The parameter (called characteristic) of the maximum derivative value of the multi scale closing or opening profile for which this maxima occurs.- The labeled classification of the input image.The behavior of the classification is :Given :math:`x_1` and :math:`x_2` two membership values,:math:`L_1, L_2` two labels associated, and :math:`\\sigma` a tolerancevalue, the following decision rule is applied::math:`L = \\begin", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.MorphologicalProfilesAnalysis"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.MorphologicalProfilesAnalysis.html"}]}, {"id": "OTB.HaralickTextureExtraction", "title": "Computes Haralick textural features on the selected channel of the input image", "description": "This application computes three sets of Haralick features [1][2]. * simple:\u00a0a set of 8 local Haralick features: Energy (texture uniformity) , Entropy (measure of randomness of intensity image), Correlation (how correlated a pixel is to its neighborhood), Inverse Difference Moment (measures the texture homogeneity), Inertia (intensity contrast between a pixel and its neighborhood), Cluster Shade, Cluster Prominence, Haralick Correlation; * advanced: a set of 10 advanced Haralick features : Mean, Variance (measures the texture heterogeneity), Dissimilarity, Sum Average, Sum Variance, Sum Entropy, Difference of Entropies, Difference of Variances, IC1, IC2; * higher: a set of 11 higher Haralick features : Short Run Emphasis (measures the texture sharpness), Long Run Emphasis (measures the texture roughness), Grey-Level Nonuniformity, Run Length Nonuniformity, Run Percentage (measures the texture sharpness homogeneity), Low Grey-Level Run Emphasis, High Grey-Level Run Emphasis, Short Run Low Grey-Level Emphasis, Short Run High Grey-Level Emphasis, Long Run Low Grey-Level Emphasis and Long Run High Grey-Level Emphasis.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.HaralickTextureExtraction"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.HaralickTextureExtraction.html"}]}, {"id": "OTB.ExtractROI", "title": "Extract a ROI defined by the user.", "description": "This application extracts a Region Of Interest with user parameters. There are four mode of extraction. The standard mode allows the user to enter one point (upper left corner of the region to extract) and a size. The extent mode needs two points (upper left corner and lower right) and the radius mode need the center of the region and the radius : it will extract the rectangle containing the circle defined and limited by the image dimension. The fit mode needs a reference image or vector and the dimension of the extracted region will be the same as the extent of the reference. Different units are available such as pixel, image physical space or longitude and latitude.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ExtractROI"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ExtractROI.html"}]}, {"id": "OTB.RefineSensorModel", "title": "Perform least-square fit of a sensor model to a set of tie points", "description": "This application reads a geom file containing a sensor model and a text file containing a list of ground control point, and performs a least-square fit of the sensor model adjustable parameters to these tie points. It produces an updated geom file as output, as well as an optional ground control points based statistics file and a vector file containing residues. The output geom file can then be used to ortho-rectify the data more accurately. Plaease note that for a proper use of the application, elevation must be correctly set (including DEM and geoid file). The map parameters allows one to choose a map projection in which the accuracy will be estimated in meters.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.RefineSensorModel"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.RefineSensorModel.html"}]}, {"id": "OTB.MultivariateAlterationDetector", "title": "Change detection by Multivariate Alteration Detector (MAD) algorithm", "description": "This application performs change detection between two multispectral images using the Multivariate Alteration Detector (MAD) [1] algorithm.The MAD algorithm produces a set of N change maps (where N is the maximum number of bands in first and second input images), with the following properties: - Change maps are differences of a pair of linear combinations of bands from image 1 and bands from image 2 chosen to maximize the correlation, - Each change map is orthogonal to the others. This is a statistical method which can handle different modalities and even different bands and number of bands between images. The application will output all change maps into a single multiband image. If numbers of bands in image 1 and 2 are equal, then change maps are sorted by increasing correlation. If number of bands is different, the change maps are sorted by decreasing correlation. The application will also print the following information:- Mean1 and Mean2 which are the mean values of bands for both input images,- V1 and V2 which are the two linear transform that are applied to input image 1 and input image 2 to build the change map,- Rho, the vector of correlation associated to each change map. The OTB filter used in this application has been implemented from the Matlab code kindly made available by the authors here [2]. Both cases (same and different number of bands) have been validated by comparing the output image to the output produced by the Matlab code, and the reference images for testing have been generated from the Matlab code using Octave.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.MultivariateAlterationDetector"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.MultivariateAlterationDetector.html"}]}, {"id": "OTB.HomologousPointsExtraction", "title": "Compute homologous points between images using keypoints", "description": "This application allows computing homologous points between images using keypoints. SIFT or SURF keypoints can be used and the band on which keypoints are computed can be set independently for both images. The application offers two modes : the first is the full mode where keypoints are extracted from the full extent of both images (please note that in this mode large image file are not supported). The second mode, called geobins, allows one to set-up spatial binning to get fewer points spread across the entire image. In this mode, the corresponding spatial bin in the second image is estimated using geographical transform or sensor modelling, and is padded according to the user defined precision. Last, in both modes the application can filter matches whose colocalisation in first image exceed this precision. The elevation parameters are to deal more precisely with sensor modelling in case of sensor geometry data. The outvector option allows creating a vector file with segments corresponding to the localisation error between the matches. It can be useful to assess the precision of a registration for instance. The vector file is always reprojected to EPSG:4326 to allow display in a GIS. This is done via reprojection or by applying the image sensor models.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.HomologousPointsExtraction"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.HomologousPointsExtraction.html"}]}, {"id": "OTB.DownloadSRTMTiles", "title": "Download or list SRTM tiles", "description": "This application allows selecting the appropriate SRTM tiles that covers a list of images. It builds a list of the required tiles. Two modes are available: the first one download those tiles from the USGS SRTM3 website (http://dds.cr.usgs.gov/srtm/version2_1/SRTM3/), the second one list those tiles in a local directory. In both cases, you need to indicate the directory in which directory tiles will be download or the location of local SRTM files.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.DownloadSRTMTiles"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.DownloadSRTMTiles.html"}]}, {"id": "OTB.TrainRegression", "title": "Train a classifier from multiple images to perform regression.", "description": "This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed between the ground truth and the estimated model. This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.TrainRegression"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.TrainRegression.html"}]}, {"id": "OTB.MorphologicalClassification", "title": "Performs morphological convex, concave and flat classification on an input image channel", "description": "This algorithm is based on the following publication:Martino Pesaresi and Jon Alti Benediktsson, Member, IEEE: A new approach for the morphological segmentation of high resolution satellite imagery.IEEE Transactions on geoscience and remote sensing, vol. 39, NO. 2, February 2001, p. 309-320.This application perform the following decision rule to classify a pixel between the three classes Convex, Concave and Flat. Let :math:`f` denote the input image and :math:`\\psi_", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.MorphologicalClassification"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.MorphologicalClassification.html"}]}, {"id": "OTB.SplitImage", "title": "Split a N multiband image into N images.", "description": "This application splits a N-bands image into N mono-band images. The output images filename will be generated from the output parameter. Thus, if the input image has 2 channels, and the user has set as output parameter, outimage.tif, the generated images will be outimage_0.tif and outimage_1.tif.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SplitImage"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SplitImage.html"}]}, {"id": "OTB.OGRLayerClassifier", "title": "Classify an OGR layer based on a machine learning model and a list of features to consider.", "description": "This application will apply a trained machine learning model on the selected feature to get a classification of each geometry contained in an OGR layer. The list of feature must match the list used for training. The predicted label is written in the user defined field for each geometry.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.OGRLayerClassifier"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.OGRLayerClassifier.html"}]}, {"id": "OTB.ImageClassifier", "title": "Performs a classification of the input image according to a model file.", "description": "This application performs an image classification based on a model file produced by the TrainImagesClassifier application. Pixels of the output image will contain the class labels decided by the classifier (maximal class label = 65535). The input pixels can be optionally centered and reduced according to the statistics file produced by the ComputeImagesStatistics application. An optional input mask can be provided, in which case only input image pixels whose corresponding mask value is greater than 0 will be classified. By default, the remaining of pixels will be given the label 0 in the output image.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ImageClassifier"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ImageClassifier.html"}]}, {"id": "OTB.VectorDataExtractROI", "title": "Perform an extract ROI on the input vector data according to the input image extent", "description": "This application extracts the vector data features belonging to a region specified by the support image envelope. Any features intersecting the support region is copied to output. The output geometries are NOT cropped.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.VectorDataExtractROI"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.VectorDataExtractROI.html"}]}, {"id": "OTB.StereoFramework", "title": "Compute the ground elevation based on one or multiple stereo pair(s)", "description": "Compute the ground elevation with a stereo block matching algorithm between one or multiple stereo pair in sensor geometry. The output is projected in desired geographic or cartographic map projection (WGS84 by default).This application is chaining different processing steps. Some of them are also performed by other applications in the stereo-reconstruction framework: * StereoRectificationGridGenerator [1] : for the generation of deformation grids * GridBasedImageResampling [2] : resampling into epipolar geometry * BlockMatching [3] : estimation of dense disparity mapsThe pipeline executes the following steps on each stereo pair: - compute the epipolar displacement grids from the stereo pair (direct and inverse) - resample the stereo pair into epipolar geometry using BCO interpolation - create masks for each epipolar image : remove black borders and resample input masks - compute horizontal disparities with a block matching algorithm - refine disparities to sub-pixel precision with a dichotomy algorithm - apply an optional median filter - filter disparities based on the correlation score and exploration bounds - translate disparities in sensor geometry - convert disparity to 3D Map.Then all 3D maps are fused to produce DSM. The fusion method in each DEM cell can be chosen between maximum, minimum and average.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.StereoFramework"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.StereoFramework.html"}]}, {"id": "OTB.ComputePolylineFeatureFromImage", "title": "This application computes the chosen descriptors for each studied polyline contained in the input VectorData.", "description": "The first step in the classifier fusion based validation is to compute the chosen descriptors for each studied polyline. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ComputePolylineFeatureFromImage"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ComputePolylineFeatureFromImage.html"}]}, {"id": "OTB.LSMSVectorization", "title": "This application performs the fourth step of the exact Large-Scale Mean-Shift segmentation workflow [1].", "description": "Given a segmentation result (label image), that may come from the LSMSSegmentation [2] application (out parameter) or have been processed for small regions merging [3] (out parameter), it will convert it to a GIS vector file containing one polygon per segment. Each polygon contains additional fields: mean and variance of each channels from input image (in parameter), segmentation image label, number of pixels in the polygon. For large images one can use the tilesizex and tilesizey parameters for tile-wise processing, with the guarantees of identical results.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.LSMSVectorization"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.LSMSVectorization.html"}]}, {"id": "OTB.SampleExtraction", "title": "Extracts samples values from an image.", "description": "The application extracts samples values from animage using positions contained in a vector data file. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SampleExtraction"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SampleExtraction.html"}]}, {"id": "OTB.GenerateRPCSensorModel", "title": "Generate a RPC sensor model from a list of Ground Control Points.", "description": "This application generates a RPC sensor model from a list of Ground Control Points. At least 20 points are required for estimation without elevation support, and 40 points for estimation with elevation support. Elevation support will be automatically deactivated if an insufficient amount of points is provided. The application can optionally output a file containing accuracy statistics for each point, and a vector file containing segments representing points residues. The map projection parameter allows defining a map projection in which the accuracy is evaluated.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.GenerateRPCSensorModel"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.GenerateRPCSensorModel.html"}]}, {"id": "OTB.Rasterization", "title": "Rasterize a vector dataset.", "description": "This application allows reprojecting and rasterize a vector dataset. The grid of the rasterized output can be set by using a reference image, or by setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset. There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry woth respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value is still used for unassociated pixels.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Rasterization"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Rasterization.html"}]}, {"id": "OTB.RigidTransformResample", "title": "Resample an image with a rigid transform", "description": "This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space). ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.RigidTransformResample"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.RigidTransformResample.html"}]}, {"id": "OTB.ComputeModulusAndPhase", "title": "This application computes the modulus and the phase of a complex SAR image.", "description": "This application computes the modulus and the phase of a complex SAR image. The input should be a single band image with complex pixels.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ComputeModulusAndPhase"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ComputeModulusAndPhase.html"}]}, {"id": "OTB.Superimpose", "title": "Using available image metadata, project one image onto another one", "description": "This application performs the projection of an image into the geometry of another one.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Superimpose"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Superimpose.html"}]}, {"id": "OTB.ConvertSensorToGeoPoint", "title": "Sensor to geographic coordinates conversion.", "description": "This Application converts a sensor point of an input image to a geographic point using the Forward Sensor Model of the input image.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ConvertSensorToGeoPoint"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ConvertSensorToGeoPoint.html"}]}, {"id": "OTB.VectorClassifier", "title": "Performs a classification of the input vector data according to a model file.", "description": "This application performs a vector data classification based on a model file produced by the TrainVectorClassifier application.Features of the vector data output will contain the class labels decided by the classifier (maximal class label = 65535). There are two modes: 1) Update mode: add of the 'cfield' field containing the predicted class in the input file. 2) Write mode: copies the existing fields of the input file in the output file and add the 'cfield' field containing the predicted class. If you have declared the output file, the write mode applies. Otherwise, the input file update mode will be applied.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.VectorClassifier"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.VectorClassifier.html"}]}, {"id": "OTB.LargeScaleMeanShift", "title": "Large-scale segmentation using MeanShift", "description": "This application chains together the 4 steps of the MeanShit framework, that is the MeanShiftSmoothing [1], the LSMSSegmentation [2], the LSMSSmallRegionsMerging [3] and the LSMSVectorization [4].This application can be a preliminary step for an object-based analysis.It generates a vector data file containing the regions extracted with the MeanShift algorithm. The spatial and range radius parameters allow adapting the sensitivity of the algorithm depending on the image dynamic and resolution. There is a step to remove small regions whose size (in pixels) is less than the given 'minsize' parameter. These regions are merged to a similar neighbor region. In the output vectors, there are additional fields to describe each region. In particular the mean and standard deviation (for each band) is computed for each region using the input image as support. If an optional 'imfield' image is given, it will be used as support image instead.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.LargeScaleMeanShift"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.LargeScaleMeanShift.html"}]}, {"id": "OTB.MeanShiftSmoothing", "title": "This application smooths an image using the MeanShift algorithm.", "description": "MeanShift [1,2,3] is an iterative edge-preserving image smoothing algorithm often used in image processing and as a first step for image segmentation. The MeanShift algorithm can be applied to multispectral images.At first iteration, for any given pixel of the input image, the filtered value correspond to the average spectral signature of neighborhood pixels that are both spatially closer than the spatial radius parameter (spatialr) and with spectral signature that have an euclidean distance to the input pixel lower than the range radius (ranger), that is, pixels that are both close in space and in spectral signatures. Subsequent iterations will repeat this process by considering that the pixel signature corresponds to the average spectral signature computed during previous iteration, and that the pixel position corresponds to the average position of pixels used to compute the average signature.The algorithm stops when the maximum number of iterations (maxiter) is reached, or when the position and spectral signature does not change much between iterations, according to the convergence threshold (thres). If the modesearch option is used then convergence will also stops if the spatial position reaches a pixel that has already converged. This will speed-up convergence, at the expense of stability of the result.The application outputs the image of the final averaged spectral signatures (fout), and can also optionally output the 2D displacement field between input pixel position and final pixel position after convergence (foutpos).Note that computing an euclidean distance between spectral signatures may be inaccurate and that techniques such as color space transform or image normalisation could be applied before using this application. Also note that most satellite images noise model is not gaussian, since noise variance linearly depends on radiance (the higher the radiance, the higher the noise variance). To account for such noise model, the application provides the range radius ramp option (rangeramp), which will vary the range radius linearly with the central pixel intensity. Default value is 1. (no ramp).This application is the first step of the large scale MeanShift method depicted in [4]. Both outputs (fout and foutpos) can be passed to the large scale MeanShift segmentation application [5]. If the application is used for large scale MeanShift, modesearch option should be off.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.MeanShiftSmoothing"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.MeanShiftSmoothing.html"}]}, {"id": "OTB.ManageNoData", "title": "Manage No-Data", "description": "This application has two modes. The first allows building a mask of no-data pixels from the no-data flags read from the image file. The second allows updating the change the no-data value of an image (pixels value and metadata). This last mode also allows replacing NaN in images with a proper no-data value. To do so, one should activate the NaN is no-data option.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ManageNoData"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ManageNoData.html"}]}, {"id": "OTB.MorphologicalMultiScaleDecomposition", "title": "Perform a geodesic morphology based image analysis on an input image channel", "description": "This application recursively apply geodesic decomposition. This algorithm is derived from the following publication:Martino Pesaresi and Jon Alti Benediktsson, Member, IEEE: A new approach for the morphological segmentation of high resolution satellite imagery.IEEE Transactions on geoscience and remote sensing, vol. 39, NO. 2, February 2001, p. 309-320.It provides a geodesic decomposition of the input image, with the following scheme. Let :math:`f_0` denote the input image, :math:`\\stackrel", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.MorphologicalMultiScaleDecomposition"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.MorphologicalMultiScaleDecomposition.html"}]}, {"id": "OTB.Smoothing", "title": "Apply a smoothing filter to an image", "description": "This application applies a smoothing filter to an image. Three methodes can be used : a gaussian filter , a mean filter , or an anisotropic diffusion using the Perona-Malik algorithm.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Smoothing"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Smoothing.html"}]}, {"id": "OTB.EdgeExtraction", "title": "This application computes edge features on every pixel of the input image selected channel", "description": "This application computes edge features on a selected channel of the input.It uses different filter such as gradient, Sobel and Touzi", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.EdgeExtraction"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.EdgeExtraction.html"}]}, {"id": "OTB.PolygonClassStatistics", "title": "Computes statistics on a training polygon set.", "description": "The application processes a set of geometries intended for training (they should have a field giving the associated class). The geometries are analyzed against a support image to compute statistics : - number of samples per class - number of samples per geometryAn optional raster mask can be used to discard samples. Different types of geometry are supported : polygons, lines, points. The behaviour is different for each type of geometry : - polygon: select pixels whose center is inside the polygon - lines : select pixels intersecting the line - points : select closest pixel to the point", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.PolygonClassStatistics"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.PolygonClassStatistics.html"}]}, {"id": "OTB.ImageEnvelope", "title": "Extracts an image envelope.", "description": "Build a vector data containing the image envelope polygon. Useful for some projection, you can set the polygon with more points with the sr parameter. This filter supports user-specified output projection. If no projection is defined, the standard WGS84 projection will be used.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ImageEnvelope"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ImageEnvelope.html"}]}, {"id": "OTB.SARCalibration", "title": "Perform radiometric calibration of SAR images. Following sensors are supported: TerraSAR-X, Sentinel1 and Radarsat-2.Both Single Look Complex(SLC) and detected products are supported as input.", "description": "The objective of SAR calibration is to provide imagery in which the pixel values can be directly related to the radar backscatter of the scene. This application allows computing Sigma Naught (Radiometric Calibration) for TerraSAR-X, Sentinel1 L1 and Radarsat-2 sensors. Metadata are automatically retrieved from image products.The application supports complex and non-complex images (SLC or detected products).", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SARCalibration"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SARCalibration.html"}]}, {"id": "OTB.SOMClassification", "title": "SOM image classification.", "description": "Unsupervised Self Organizing Map image classification.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SOMClassification"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SOMClassification.html"}]}, {"id": "OTB.ReadImageInfo", "title": "Get information about the image", "description": "Display information about the input image like: image size, origin, spacing, metadata, projections...", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ReadImageInfo"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ReadImageInfo.html"}]}, {"id": "OTB.VectorDataTransform", "title": "Apply a transform to each vertex of the input VectorData", "description": "This application iterates over each vertex in the input vector data file and performs a transformation on this vertex.It is the equivalent of [1] that transforms images. For instance, if you extract the envelope of an image with [2], and you transform this image with [1], you may want to use this application to operate the same transform on the envelope.The applied transformation is a 2D similarity. It manages translation, rotation, scaling, and can be centered or not. Note that the support image is used to define the reference coordinate system in which the transform is applied. For instance the input vector data can have WGS84 coordinates, the support image is in UTM, so a translation of 1 pixel along X corresponds to the X pixel size of the input image along the X axis of the UTM coordinates frame. This image can also be in sensor geometry.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.VectorDataTransform"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.VectorDataTransform.html"}]}, {"id": "OTB.ConvertCartoToGeoPoint", "title": "Convert cartographic coordinates to geographic ones.", "description": "This application computes the geographic coordinates from cartographic ones. User has to give the X and Y coordinate and the cartographic projection (see mapproj parameter for details).", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ConvertCartoToGeoPoint"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ConvertCartoToGeoPoint.html"}]}, {"id": "OTB.VectorDataDSValidation", "title": "Vector data validation based on the fusion of features using Dempster-Shafer evidence theory framework.", "description": "This application validates or unvalidate the studied samples using the Dempster-Shafer theory.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.VectorDataDSValidation"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.VectorDataDSValidation.html"}]}, {"id": "OTB.Convert", "title": "Convert an image to a different format, optionally rescaling the data and/or changing the pixel type.", "description": "This application performs an image pixel type conversion (short, ushort, uchar, int, uint, float and double types are handled). The output image is written in the specified format (ie. that corresponds to the given extension). The conversion can include a rescale of the data range, by default it's set from 23777675706674240 9822445070020f the data values. The rescale can be linear or log2. The choice of the output channels can be done with the extended filename, but less easy to handle. To do this, a 'channels' parameter allows you to select the desired bands at the output. There are 3 modes, the available choices are: * grayscale : to display mono image as standard color image * rgb : select 3 bands in the input image (multi-bands) * all : keep all bands.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Convert"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Convert.html"}]}, {"id": "OTB.SARPolarMatrixConvert", "title": "This applications allows converting classical polarimetric matrices to each other.", "description": "This application allows converting classical polarimetric matrices to each other.For instance, it is possible to get the coherency matrix from the Sinclar one, or the Mueller matrix from the coherency one.The filters used in this application never handle matrices, but images where each band is related to their elements.As most of the time SAR polarimetry handles symmetric/hermitian matrices, only the relevant elements are stored, so that the images representing them have a minimal number of bands.For instance, the coherency matrix size is 3x3 in the monostatic case, and 4x4 in the bistatic case : it will thus be stored in a 6-band or a 10-band complex image (the diagonal and the upper elements of the matrix).The Sinclair matrix is a special case : it is always represented as 3 or 4 one-band complex images (for mono- or bistatic case).The available conversions are listed below:--- Monostatic case ---1 msinclairtocoherency --> Sinclair matrix to coherency matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels)2 msinclairtocovariance --> Sinclair matrix to covariance matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels)3 msinclairtocircovariance --> Sinclair matrix to circular covariance matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels)4 mcoherencytomueller --> Coherency matrix to Mueller matrix (input : 6 complex channels | 16 real channels)5 mcovariancetocoherencydegree --> Covariance matrix to coherency degree (input : 6 complex channels | 3 complex channels)6 mcovariancetocoherency --> Covariance matrix to coherency matrix (input : 6 complex channels | 6 complex channels)7 mlinearcovariancetocircularcovariance --> Covariance matrix to circular covariance matrix (input : 6 complex channels | output : 6 complex channels)--- Bistatic case ---8 bsinclairtocoherency --> Sinclair matrix to coherency matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | 10 complex channels)9 bsinclairtocovariance --> Sinclair matrix to covariance matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 10 complex channels)10 bsinclairtocircovariance --> Sinclair matrix to circular covariance matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 10 complex channels)--- Both cases ---11 sinclairtomueller --> Sinclair matrix to Mueller matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 16 real channels)12 muellertomcovariance --> Mueller matrix to covariance matrix (input : 16 real channels | output : 6 complex channels)13 muellertopoldegandpower --> Mueller matrix to polarization degree and power (input : 16 real channels | output : 4 real channels)", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SARPolarMatrixConvert"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SARPolarMatrixConvert.html"}]}, {"id": "OTB.ObtainUTMZoneFromGeoPoint", "title": "UTM zone determination from a geographic point.", "description": "This application returns the UTM zone of an input geographic point.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ObtainUTMZoneFromGeoPoint"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ObtainUTMZoneFromGeoPoint.html"}]}, {"id": "OTB.Pansharpening", "title": "Perform P+XS pansharpening", "description": "This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Pansharpening"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Pansharpening.html"}]}, {"id": "OTB.VectorDataReprojection", "title": "Reproject a vector data using support image projection reference, or a user specified map projection", "description": "This application allows reprojecting a vector data using support image projection reference, or a user given map projection. If given, image keywordlist can be added to reprojected vectordata.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.VectorDataReprojection"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.VectorDataReprojection.html"}]}, {"id": "OTB.ClassificationMapRegularization", "title": "Filters the input labeled image using Majority Voting in a ball shaped neighbordhood.", "description": "This application filters the input labeled image (with a maximal class label = 65535) using Majority Voting in a ball shaped neighbordhood. Majority Voting takes the more representative value of all the pixels identified by the ball shaped structuring element and then sets the center pixel to this majority label value. -NoData is the label of the NOT classified pixels in the input image. These input pixels keep their NoData label in the output image. -Pixels with more than 1 majority class are marked as Undecided if the parameter 'ip.suvbool == true', or keep their Original labels otherwise.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ClassificationMapRegularization"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ClassificationMapRegularization.html"}]}, {"id": "OTB.PixelValue", "title": "Get the value of a pixel.", "description": "This application gives the value of a selected pixel. There are three ways to designate a pixel, with its index, its physical coordinate (in the physical space attached to the image), and with geographical coordinate system. Coordinates will be interpreted differently depending on which mode is chosen.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.PixelValue"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.PixelValue.html"}]}, {"id": "OTB.GeneratePlyFile", "title": "Generate a 3D Ply file from a DEM and a color image.", "description": "The application converts an image containing elevations into a PLY file, which is a file format to store 3D models. This format is adpated for visualization on software such as MeshLab [2] or CloudCompare [3]This application is part of the stereo reconstruction framework. The input data can be produced by the application DisparityMapToElevationMap.There are two types of supported input images: * A DEM image, with a ground projection, containing elevation values. Each elevation value can be considered as a 3D point. * A 3D grid image, containing 5 bands (the first 3 are the 3D coordinates of each point, the 5th is a validity mask where valid values are larger or equal to 1)The user shall also give a support image that contains color values for each 3D point. The color values will be embedded in the PLY file.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.GeneratePlyFile"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.GeneratePlyFile.html"}]}, {"id": "OTB.LocalStatisticExtraction", "title": "Computes local statistical moments on every pixel in the selected channel of the input image", "description": "This application computes the 4 local statistical moments on every pixel in the selected channel of the input image, over a specified neighborhood. The output image is multi band with one statistical moment (feature) per band. Thus, the 4 output features are the Mean, the Variance, the Skewness and the Kurtosis. They are provided in this exact order in the output image.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.LocalStatisticExtraction"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.LocalStatisticExtraction.html"}]}, {"id": "OTB.TrainVectorClassifier", "title": "Train a classifier based on labeled geometries and a list of features to consider.", "description": "This application trains a classifier based on labeled geometries and a list of features to consider for classification.This application is based on LibSVM, OpenCV Machine Learning (2.3.1 and later), and Shark ML The output of this application is a text model file, whose format corresponds to the ML model type chosen. There is no image nor vector data output.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.TrainVectorClassifier"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.TrainVectorClassifier.html"}]}, {"id": "OTB.HooverCompareSegmentation", "title": "Compare two segmentations with Hoover metrics", "description": "This application compares a machine segmentation (MS) with a partial ground truth segmentation (GT). The Hoover metrics are used to estimate scores for correct detection, over-segmentation, under-segmentation and missed detection. The application can output the overall Hoover scores along with coloredimages of the MS and GT segmentation showing the state of each region (correct detection, over-segmentation, under-segmentation, missed) The Hoover metrics are described in : Hoover et al., \"An experimental comparison of range image segmentation algorithms\", IEEE PAMI vol. 18, no. 7, July 1996.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.HooverCompareSegmentation"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.HooverCompareSegmentation.html"}]}, {"id": "OTB.ContrastEnhancement", "title": "This application is the implementation of the histogram equalization algorithm. It can be used to enhance contrast in an image or to reduce the dynamic of the image without losing too much contrast. It offers several options as a no data value, a contrast limitation factor, a local version of the algorithm and also a mode to equalize the luminance of the image.", "description": "This application is the implementation of the histogram equalization algorithm. The idea of the algorithm is to use the whole available dynamic. In order to do so it computes a histogram over the image and then use the whole dynamic: meaning flattening the histogram. That gives us gain for each bin that transform the original histogram into the flat one. This gain is then apply on the original image.The application proposes several options to allow a finer result: - There is an option to limit contrast. We choose to limit the contrast by modifying the original histogram. To do so we clip the histogram at a given height and redistribute equally among the bins the clipped population. Then we add a local version of the algorithm. - It is possible to apply the algorithm on tiles of the image, instead of on the whole image. That gives us gain depending on the value of the pixel and its position in the image. In order to smoothen the result we interpolate the gain between tiles.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ContrastEnhancement"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ContrastEnhancement.html"}]}, {"id": "OTB.ComputeOGRLayersFeaturesStatistics", "title": "Compute statistics of the features in a set of OGR Layers", "description": "Compute statistics (mean and standard deviation) of the features in a set of OGR Layers, and write them in an XML file. This XML file can then be used by the training application.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ComputeOGRLayersFeaturesStatistics"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ComputeOGRLayersFeaturesStatistics.html"}]}, {"id": "OTB.MultiImageSamplingRate", "title": "Compute sampling rate for an input set of images.", "description": "The application computes sampling rates for a set of input images. Before calling this application, each pair of image and training vectors has to be analysed with the application PolygonClassStatistics. The statistics file is then used to compute the sampling rates for each class in each image. Several types of sampling are implemented. Each one is a combination of a mono-image strategy and a multi-image mode. The mono-image strategies are : * smallest (default) : select the same number of sample in each class so that the smallest one is fully sampled. * constant : select the same number of samples N in each class (with N below or equal to the size of the smallest class). * byclass : set the required number for each class manually, with an input CSV file (first column is class name, second one is the required samples number).The multi-image modes (mim) are proportional, equal and custom. The custom mode lets the users choose the distribution of samples among the images. The different behaviours are described below. Ti(c) and Ni(c) refers resp. to the total number and needed number of samples in image i for class c. Let's call L the total number of images. * strategy = all - Same behaviour for all modes : take all samples * strategy = constant : let's call M the global number of samples required per class. For each image i and each class c: - if mim = proportional, then Ni( c ) = M * Ti( c ) / sum_k( Tk(c) ) - if mim = equal , then Ni( c ) = M / L - if mim = custom , then Ni( c ) = Mi where Mi is the custom requested number of samples for image i * strategy = byClass : let's call M(c) the global number of samples for class c). For each image i and each class c: - if mim = proportional, then Ni( c ) = M(c) * Ti( c ) / sum_k( Tk(c) ) - if mim = equal , then Ni( c ) = M(c) / L - if mim = custom , then Ni( c ) = Mi(c) where Mi(c) is the custom requested number of samples for image i and class c * strategy = percent : For each image i and each class c: - if mim = proportional, then Ni( c ) = p * Ti( c ) where p is the global percentage of samples - if mim = equal , then Ni( c ) = p * sum_k(Tk(c)]/L where p is the global percentage of samples - if mim = custom , then Ni( c ) = p(i) * Ti(c) where p(i) is the percentage of samples for image i. c * strategy = total : For each image i and each class c: - if mim = proportional, then Ni( c ) = total * (sum_k(Ti(k))/sum_kl(Tl(k))) * (Ti(c)/sum_k(Ti(k))) where total is the total number of samples specified. - if mim = equal , then Ni( c ) = (total / L) * (Ti(c)/sum_k(Ti(k))) where total is the total number of samples specified. - if mim = custom , then Ni( c ) = total(i) * (Ti(c)/sum_k(Ti(k))) where total(i) is the total number of samples specified for image i. * strategy = smallest class - if mim = proportional, then the smallest class size (computed globally) is used for the strategy constant+proportional. - if mim = equal , then the smallest class size (computed globally) is used for the strategy constant+equal. - if mim = custom , then the smallest class is computed and used for each image separately.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.MultiImageSamplingRate"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.MultiImageSamplingRate.html"}]}, {"id": "OTB.FineRegistration", "title": "Estimate disparity map between two images.", "description": "This application computes a disparity map between two images that correspond to the same scene. It is intended for case where small misregistration between images should be estimated and fixed. The search is performed in 2D.The algorithm uses an iterative approach to estimate a best match between local patches. The typical use case is registration betwween similar bands, or between two acquisitions. The output image contains X and Y offsets, as well as the metric value. A sub-pixel accuracy can be expected. The input images should have the same size and same physical space.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.FineRegistration"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.FineRegistration.html"}]}, {"id": "OTB.VertexComponentAnalysis", "title": "Given a set of mixed spectral vectors, estimatereference substances also known as endmembers using the VertexComponent Analysis algorithm.", "description": "Apply the Vertex Component Analysis [1] toan hyperspectral image to extract endmembers. Given a set of mixedspectral vectors (multispectral or hyperspectral), the applicationestimates the spectral signature of reference substances also knownas endmembers.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.VertexComponentAnalysis"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.VertexComponentAnalysis.html"}]}, {"id": "OTB.SARPolarSynth", "title": "Gives, for each pixel, the power that would have been received by a SAR system with a basis different from the classical (H,V) one (polarimetric synthetis).", "description": "This application gives, for each pixel, the power that would have been received by a SAR system with a basis different from the classical (H,V) one (polarimetric synthetis).The new basis A and B are indicated through two Jones vectors, defined by the user thanks to orientation (psi) and ellipticity (khi) parameters.These parameters are namely psii, khii, psir and khir. The suffixes (i) and (r) refer to the transmitting antenna and the receiving antenna respectively.Orientations and ellipticities are given in degrees, and are between -90/90 degrees and -45/45 degrees respectively. Four polarization architectures can be processed : 1. HH_HV_VH_VV : full polarization, general bistatic case.2. HH_HV_VV or HH_VH_VV : full polarization, monostatic case (transmitter and receiver are co-located).3. HH_HV : dual polarization.4. VH_VV : dual polarization.The application takes a complex vector image as input, where each band correspond to a particular emission/reception polarization scheme.User must comply with the band order given above, since the bands are used to build the Sinclair matrix.In order to determine the architecture, the application first relies on the number of bands of the input image.1. Architecture HH_HV_VH_VV is the only one with four bands, there is no possible confusion.2. Concerning HH_HV_VV and HH_VH_VV architectures, both correspond to a three channels image. But they are processed in the same way, as the Sinclair matrix is symmetric in the monostatic case.3. Finally, the two last architectures (dual polarizations), can't be distinguished only by the number of bands of the input image. User must then use the parameters emissionh and emissionv to indicate the architecture of the system : emissionh=1 and emissionv=0 --> HH_HV, emissionh=0 and emissionv=1 --> VH_VV.Note : if the architecture is HH_HV, khii and psii are automatically both set to 0 degree; if the architecture is VH_VV, khii and psii are automatically set to 0 degree and 90 degrees respectively.It is also possible to force the calculation to co-polar or cross-polar modes.In the co-polar case, values for psir and khir will be ignored and forced to psii and khii; same as the cross-polar mode, where khir and psir will be forced to (psii + 90 degrees) and -khii.Finally, the result of the polarimetric synthetis is expressed in the power domain, through a one-band scalar image.Note: this application doesn't take into account the terms which do not depend on the polarization of the antennas. The parameter gain can be used for this purpose.More details can be found in the OTB CookBook (SAR processing chapter).", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SARPolarSynth"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SARPolarSynth.html"}]}, {"id": "OTB.HyperspectralUnmixing", "title": "Estimate abundance maps from an hyperspectral image and a set of endmembers.", "description": "The application applies a linear unmixing algorithmto an hyperspectral data cube. This method supposes that the mixture betweenaterials in the scene is macroscopic and simulates a linear mixing model ofspectra.The Linear Mixing Model (LMM) acknowledges that reflectancespectrum associated with each pixel is a linear combination of purematerials in the recovery area, commonly known as endmembers. Endmembers canbe estimated using the VertexComponentAnalysis application.The application allows estimating the abundance maps with several algorithms : * Unconstrained Least Square (ucls) * Image Space Reconstruction Algorithm (isra) * Non-negative constrained * Least Square (ncls) * Minimum Dispersion Constrained Non Negative Matrix Factorization (MDMDNMF).", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.HyperspectralUnmixing"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.HyperspectralUnmixing.html"}]}, {"id": "OTB.TestApplication", "title": "This application helps developers to test parameters types", "description": "The purpose of this application is to test parameters types.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.TestApplication"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.TestApplication.html"}]}, {"id": "OTB.Segmentation", "title": "Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.", "description": "This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output.In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colours. Please note that this mode loads the whole input image into memory, and as such can not handle large images. To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Segmentation"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Segmentation.html"}]}, {"id": "OTB.DSFuzzyModelEstimation", "title": "Estimate feature fuzzy model parameters using 2 vector data (ground truth samples and wrong samples).", "description": "Estimate feature fuzzy model parameters using 2 vector data (ground truth samples and wrong samples).", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.DSFuzzyModelEstimation"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.DSFuzzyModelEstimation.html"}]}, {"id": "OTB.CompareImages", "title": "Estimator between 2 images.", "description": "This application computes MSE (Mean Squared Error), MAE (Mean Absolute Error) and PSNR (Peak Signal to Noise Ratio) between the channel of two images (reference and measurement). The user has to set the used channel and can specify a ROI.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.CompareImages"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.CompareImages.html"}]}, {"id": "OTB.ColorMapping", "title": "Maps an input label image to 8-bits RGB using look-up tables.", "description": "This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges.-The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - The support image method uses a color support image to associate an average color to each region.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ColorMapping"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ColorMapping.html"}]}, {"id": "OTB.Rescale", "title": "Rescale the image between two given values.", "description": "This application scales the given image pixel intensity between two given values.By default min (resp. max) value is set to 0 (resp. 255).Input minimum and maximum values is automatically computed for all image bands.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Rescale"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.Rescale.html"}]}, {"id": "OTB.GrayScaleMorphologicalOperation", "title": "Performs morphological operations on a grayscale input image", "description": "This application performs grayscale morphological operations on a mono band image", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.GrayScaleMorphologicalOperation"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.GrayScaleMorphologicalOperation.html"}]}, {"id": "OTB.ConcatenateVectorData", "title": "Concatenate vector data files", "description": "This application concatenates a list of vector data files to produce a unique vector data output file.This application will gather all the geometries from the input files and write them into an output vector data file. Any format supported by OGR can be used. Ideally, all inputs should have the same set of fields and the same spatial reference system.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ConcatenateVectorData"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ConcatenateVectorData.html"}]}, {"id": "OTB.ComputeImagesStatistics", "title": "Computes global mean and standard deviation for each band from a set of images and optionally saves the results in an XML file.", "description": "This application computes a global mean and standard deviation for each band of a set of images and optionally saves the results in an XML file. The output XML is intended to be used as an input for the TrainImagesClassifier application to normalize samples before learning. You can also normalize the image with the XML file in the ImageClassifier application.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ComputeImagesStatistics"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.ComputeImagesStatistics.html"}]}, {"id": "OTB.SampleSelection", "title": "Selects samples from a training vector data set.", "description": "The application selects a set of samples from geometries intended for training (they should have a field giving the associated class). First of all, the geometries must be analyzed by the PolygonClassStatistics application to compute statistics about the geometries, which are summarized in an xml file. Then, this xml file must be given as input to this application (parameter instats).The input support image and the input training vectors shall be given in parameters 'in' and 'vec' respectively. Only the sampling grid (origin, size, spacing)will be read in the input image.There are several strategies to select samples (parameter strategy) : - smallest (default) : select the same number of sample in each class so that the smallest one is fully sampled. - constant : select the same number of samples N in each class (with N below or equal to the size of the smallest class). - byclass : set the required number for each class manually, with an input CSV file (first column is class name, second one is the required samples number). - percent: set a target global percentage of samples to use. Class proportions will be respected. - total: set a target total number of samples to use. Class proportions will be respected. There is also a choice on the sampling type to performs : - periodic : select samples uniformly distributed - random : select samples randomly distributedOnce the strategy and type are selected, the application outputs samples positions(parameter out).The other parameters to look at are : - layer : index specifying from which layer to pick geometries. - field : set the field name containing the class. - mask : an optional raster mask can be used to discard samples. - outrates : allows outputting a CSV file that summarizes the sampling rates for each class.As with the PolygonClassStatistics application, different types of geometry are supported : polygons, lines, points. The behavior of this application is different for each type of geometry : - polygon: select points whose center is inside the polygon - lines : select points intersecting the line - points : select closest point to the provided point", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SampleSelection"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.SampleSelection.html"}]}, {"id": "OTB.BundleToPerfectSensor", "title": "Perform P+XS pansharpening", "description": "This application performs P+XS pansharpening. The default mode use Pan and XS sensor models to estimate the transformation to superimpose XS over Pan before the fusion (\"default mode\"). The application provides also a PHR mode for Pleiades images which does not use sensor models as Pan and XS products are already coregistered but only estimate an affine transformation to superimpose XS over the Pan.Note that this option is automatically activated in case Pleiades images are detected as input.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.BundleToPerfectSensor"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/OTB.BundleToPerfectSensor.html"}]}, {"id": "IsValid", "title": "IsValid test ", "description": "This service shall return a TRUE value if and only if the geometry g is valid", "version": "2.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/IsValid"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/IsValid.html"}]}, {"id": "Ogr2Ogr", "title": "Converts vector data from one format to another. ", "description": "http://www.gdal.org/ogr2ogr.html", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Ogr2Ogr"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Ogr2Ogr.html"}]}, {"id": "Union", "title": "Compute union. ", "description": "This service shall return a bag of geometry values representing a Point set union of geometry InputEntity1 with geometry InputEntity2.", "version": "2.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Union"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Union.html"}]}, {"id": "hellor", "title": "HelloWorld Service in R", "description": "Output and Hello Wolrd string", "version": "2.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/hellor"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/hellor.html"}]}, {"id": "HelloPy", "title": "Create a welcome message string.", "description": "Create a welcome string.", "version": "2.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/HelloPy"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/HelloPy.html"}]}, {"id": "Gdal_Contour", "title": "Builds vector contour lines from a raster elevation model.", "description": "http://www.gdal.org/gdal_contour.html", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Gdal_Contour"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Gdal_Contour.html"}]}, {"id": "Gdal_Warp", "title": "GDAL Warp Tool", "description": "The gdalwarp utility is an image mosaicing, reprojection and warping utility. The program can reproject to any supported projection, and can also apply GCPs stored with the image if the image is \"raw\" with control information.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Gdal_Warp"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Gdal_Warp.html"}]}, {"id": "GdalExtractProfile", "title": "Extract elevation values along a line. ", "description": "Fetch the x,y,z values of a DEM raster file along a linestring", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/GdalExtractProfile"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/GdalExtractProfile.html"}]}, {"id": "hellojs", "title": "HelloWorld Service in JavaScript", "description": "Output and Hello Wolrd string", "version": "2.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/hellojs"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/hellojs.html"}]}, {"id": "Within", "title": "Within test", "description": "This service shall return a true value if and only if the geometry InputEntity1 is spatially within geometry InputEntity2; that is if every point on InputEntity1 is also on InputEntity2.", "version": "2.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Within"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Within.html"}]}, {"id": "Buffer", "title": "Create a buffer around a polygon. ", "description": "This service shall return a feature collection representing the buffer of geometry InputPolygon at distance BufferDistance. The buffer of a geometry at distance d is the Polygon or MultiPolygon which contains all points within a distance d of the geometry.", "version": "2.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Buffer"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/Buffer.html"}]}, {"id": "GetStatus", "title": "Produce an updated ExecuteResponse document. ", "description": "Create an ExecuteResponse document from a sid (Service ID), it will use the niternal ZOO Kernel mechanisms to access the current status from a running Service and update the percentCompleted from the original backup file used by the ZOO Kernel when running a Service in background. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/GetStatus"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/GetStatus.html"}]}, {"id": "SAGA.shapes_transect.0", "title": "Transect through polygon shapefile", "description": "Transect for lines and polygon shapefiles<br/><br/>The goal of this tool is to create a transect along a line through a polygon map.<br/>Eg<br/><br/>|____ST1_____!_ST2_!__ST1__!_______ST#_____|<br/><br/>(Soil type 1 etc...)<br/><br/>This is done by creating a table with the ID of each line, the distance <br/>to the starting point and the different transects:<br/><br/><pre>| line_id | start | end | code/field |<br/>| 0 | 0 | 124 | ST1 |<br/>| 0 | 124 | 300 | ST2 |<br/>| 0 | 300 | 1223 | ST1 |<br/>| 0 | 1223 | 2504 | ST3 |<br/>| 1 | 0 | 200 | ST4 |<br/>| ... | ... | ... | ... |</pre><br/><br/><br/>The tool requires an input shape with all the line transects [Transect_Line] <br/>and a polygon theme [Theme]. You also have to select which field you want to have in <br/>the resulting table [Transect_Result]. This can be an ID of the polygon theme if you <br/>want to link the tables later on, or any other field [Theme_Field].<br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.shapes_transect.0"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.shapes_transect.0.html"}]}, {"id": "SAGA.sim_ecosystems_hugget.1", "title": "02: Carbon Cycle Simulation for Terrestrial Biomass", "description": "Simulation of the Carbon Cycle in Terrestrial Biomass. <br/>Reference:<br/>Hugget, R.J. (1993): 'Modelling the Human Impact on Nature', Oxford University Press.<br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_ecosystems_hugget.1"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_ecosystems_hugget.1.html"}]}, {"id": "SAGA.sim_ecosystems_hugget.2", "title": "03: Spatially Distributed Simulation of Soil Nitrogen Dynamics", "description": "Spatially Distributed Simulation of Soil Nitrogen Dynamics. <br/>Reference:<br/>Hugget, R.J. (1993): 'Modelling the Human Impact on Nature', Oxford University Press.<br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_ecosystems_hugget.2"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_ecosystems_hugget.2.html"}]}, {"id": "SAGA.sim_ecosystems_hugget.0", "title": "01: A Simple Litter System", "description": "A simple litter system model using the euler method. Carbon storage C is calculated in dependency of litter fall rate (Cinput) and rate constant for litter loss (Closs) as:<br/>C(t + 1) = C(t) + (Cinput - Closs * C(t)) * dt<br/><br/>Typical values:<br/><br/>- Tropical Rainforest:<br/>-- Litter fall rate: 500 [g/m<sup>2</sup>/a]<br/>-- Litter loss rate: 2.0 [1/a]<br/><br/>- Temperate forest:<br/>-- Litter fall rate: 240 [g/m<sup>2</sup>/a]<br/>-- Litter loss rate: 0.4 [1/a]<br/><br/>- Boreal forest:<br/>-- Litter fall rate: 50 [g/m<sup>2</sup>/a]<br/>-- Litter loss rate: 0.05 [1/a]<br/><br/>Reference:<br/>Hugget, R.J. (1993): 'Modelling the Human Impact on Nature', Oxford University Press.<br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_ecosystems_hugget.0"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_ecosystems_hugget.0.html"}]}, {"id": "SAGA.grid_filter.18", "title": "Simple Filter (Restricted to Polygons)", "description": "Simple standard filters for grids, evaluation within polygons.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.18"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.18.html"}]}, {"id": "SAGA.grid_filter.10", "title": "Mesh Denoise", "description": "Mesh denoising for grids, using the algorithm of Sun et al. (2007).<br/>References:<br/>Cardiff University: Filtering and Processing of Irregular Meshes with Uncertainties. <a target=\"_blank\" href=\"http://www.cs.cf.ac.uk/meshfiltering/\">online</a>.<br/>Stevenson, J.A., Sun, X., Mitchell, N.C. (2010): Despeckling SRTM and other topographic data with a denoising algorithm, Geomorphology, Vol.114, No.3, pp.238-252.<br/>Sun, X., Rosin, P.L., Martin, R.R., Langbein, F.C. (2007): Fast and effective feature-preserving mesh denoising. IEEE Transactions on Visualization and Computer Graphics, Vol.13, No.5, pp.925-938.<br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.10"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.10.html"}]}, {"id": "SAGA.grid_filter.7", "title": "DTM Filter (slope-based)", "description": "The tool can be used to filter a digital surface model (DSM), i.e. to classify its cells into bare earth and object cells (ground and nonground cells).<br/><br/>The tool uses concepts described by VOSSELMAN (2000) and is based on the assumption that a large height difference between two nearby cells is unlikely to be caused by a steep slope in the terrain. The probability that the higher cell could be a ground point decreases if the distance between the two cells decreases. Therefore the filter defines the acceptable height difference between two cells as a function of the distance between the cells. A cell is classified as terrain if there is no other cell within the kernel search radius such that the height difference between these cells is larger than the allowed maximum height difference at the distance between these cells.<br/><br/>The approximate terrain slope parameter is used to modify the filter function to match the overall slope in the study area. A confidence interval may be used to reject outliers.<br/><br/>Reference:<br/>VOSSELMAN, G. (2000): Slope based filtering of laser altimetry data. IAPRS, Vol. XXXIII, Part B3, Amsterdam, The Netherlands. pp. 935-942<br/><br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.7"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.7.html"}]}, {"id": "SAGA.grid_filter.12", "title": "Geodesic Morphological Reconstruction", "description": "Geodesic morphological reconstruction according to <br/>L. Vincent (1993): Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms. IEEE Transactions on Image Processing, Vol. 2, No 2<br/>Here we use the algorithm on p. 194: Computing of Regional Maxima and Breadth-first Scanning.<br/><br/>A marker is derived from the input image INPUT_GRID by subtracting a constant SHIFT_VALUE. Optionally the SHIFT_VALUE can be set to zero at the border of the grid (\"Preserve 1px border Yes/No\"). OUTPUT_GRID is the difference between the input image and the morphological reconstruction of the marker under the input image as mask. If the Option \"Create a binary mask\" is selected, the OUTPUT_GRID is thresholded with THRESHOLD, creating a binary image of maxima regions.<br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.12"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.12.html"}]}, {"id": "SAGA.grid_filter.6", "title": "Majority/Minority Filter", "description": "Majority filter for grids.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.6"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.6.html"}]}, {"id": "SAGA.grid_filter.5", "title": "Filter Clumps", "description": "(c) 2004 by Victor Olaya. Filter Clumps", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.5"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.5.html"}]}, {"id": "SAGA.grid_filter.1", "title": "Gaussian Filter", "description": "The Gaussian filter is a smoothing operator that is used to 'blur' or 'soften' data and to remove detail and noise. The degree of smoothing is determined by the standard deviation. For higher standard deviations you need to use a larger search radius.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.1"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.1.html"}]}, {"id": "SAGA.grid_filter.3", "title": "Multi Direction Lee Filter", "description": "The tool searches for the minimum variance within 16 directions and applies a Lee Filter in the direction of minimum variance. The filter is edge-preserving and can be used to remove speckle noise from SAR images or to smooth DTMs. Applied to DTMs, this filter will preserve slope breaks and narrow valleys.<br/>For more details, please refer to the references. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.3"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.3.html"}]}, {"id": "SAGA.grid_filter.2", "title": "Laplacian Filter", "description": "Other Common Names: Laplacian, Laplacian of Gaussian, LoG, Marr Filter<br/><br/>Standard kernel 1 (3x3):<br/> 0 | -1 | 0<br/>-- + -- + --<br/>-1 | 4 | -1<br/>-- + -- + --<br/> 0 | -1 | 0<br/><br/>Standard kernel 2 (3x3):<br/>-1 | -1 | -1<br/>-- + -- + --<br/>-1 | 8 | -1<br/>-- + -- + --<br/>-1 | -1 | -1<br/><br/>Standard kernel 3 (3x3):<br/>-1 | -2 | -1<br/>-- + -- + --<br/>-2 | 12 | -2<br/>-- + -- + --<br/>-1 | -2 | -1<br/><br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.2"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.2.html"}]}, {"id": "SAGA.grid_filter.16", "title": "Wombling (Edge Detection)", "description": "Continuous Wombling for edge detection. Uses magnitude of gradient to detect edges between adjacent cells. Edge segments connect such edges, when the difference of their gradient directions is below given threshold.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.16"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.16.html"}]}, {"id": "SAGA.grid_filter.14", "title": "Connectivity Analysis", "description": "Connectivity analysis of a binary input image according to <br/>Burger, W., Burge, M.: Digitale Bildverarbeitung. Springer Verlag 2006, p.208.<br/>Output consists in a symbolic image of the connected foreground regions and a shape of the borders of the foreground regions (outer and inner borders). The shape may contain alternatively the centers or the corners of the border pixels. Optionally, the regions which have contact with the image borders can be removed together with their border shapes. <br/>In addition, an optional morphological filter (erosion-binary reconstruction) can be applied to the input image first. <br/><br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.14"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.14.html"}]}, {"id": "SAGA.grid_filter.8", "title": "Morphological Filter", "description": "Morphological filter for grids. Dilation returns the maximum and erosion the minimum value found in a cell's neighbourhood as defined by the kernel. Opening applies first an erosion followed by a dilation and closing is a dilation followed by an erosion. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.8"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.8.html"}]}, {"id": "SAGA.grid_filter.0", "title": "Simple Filter", "description": "Simple standard filters for grids.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.0"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.0.html"}]}, {"id": "SAGA.grid_filter.11", "title": "Resampling Filter", "description": "Resampling filter for grids. Resamples in a first step the given grid to desired resampling cell size, expressed as multiple of the original cell size (scale factor). This is an up-scaling through which cell values are aggregated as cell area weighted means. Second step is the down-scaling to original cell size using spline interpolation. Specially for larger search distances this is a comparably fast alternative for simple low and high pass filter operations. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.11"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.11.html"}]}, {"id": "SAGA.grid_filter.4", "title": "User Defined Filter", "description": "User defined filter matrix. The filter can be chosen from loaded tables. If not specified a fixed table with 3 rows (and 3 columns) will be used. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.4"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.4.html"}]}, {"id": "SAGA.grid_filter.9", "title": "Rank Filter", "description": "Rank filter for grids. Set rank to fifty percent to apply a median filter.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.9"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.9.html"}]}, {"id": "SAGA.grid_filter.15", "title": "Sieve Classes", "description": "The 'Sieve Classes' tool counts the number of adjacent cells sharing the same value (the class identifier). Areas that are formed by less cells than specified by the treshold will be removed (sieved), i.e. they are set to no-data. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.15"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.15.html"}]}, {"id": "SAGA.grid_filter.17", "title": "Wombling for Multiple Features (Edge Detection)", "description": "Continuous Wombling for edge detection. Uses magnitude of gradient to detect edges between adjacent cells. Edge segments connect such edges, when the difference of their gradient directions is below given threshold.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.17"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.17.html"}]}, {"id": "SAGA.grid_filter.13", "title": "Binary Erosion-Reconstruction", "description": "Common binary Opening does not guarantee, that foreground regions which outlast the erosion step are reconstructed to their original shape in the dilation step. Depending on the application, that might be considered as a deficiency. Therefore this tool provides a combination of erosion with the binary Geodesic Morphological Reconstruction, see <br/>L. Vincent (1993): Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms. IEEE Transactions on Image Processing, Vol. 2, No 2<br/>Here we use the algorithm on p. 194: Breadth-first Scanning.<br/><br/>The marker is defined as the eroded INPUT_GRID, whereas the mask is just the INPUT_GRID itself. OUTPUT_GRID is the reconstruction of the marker under the mask.<br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.13"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_filter.13.html"}]}, {"id": "SAGA.table_calculus.18", "title": "Aggregate Values by Attributes", "description": "Aggregate Values by Attributes", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.18"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.18.html"}]}, {"id": "SAGA.table_calculus.7", "title": "Principal Component Analysis", "description": "Principal Component Analysis (PCA) for tables. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.7"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.7.html"}]}, {"id": "SAGA.table_calculus.12", "title": "Minimum Redundancy Feature Selection", "description": "Identify the most relevant features for subsequent classification of tabular data.<br/><br/>The minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm has been developed by Hanchuan Peng <hanchuan.peng@gmail.com>.<br/><br/>References:<br/>Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. Hanchuan Peng, Fuhui Long, and Chris Ding, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp.1226-1238, 2005.<br/><br/>Minimum redundancy feature selection from microarray gene expression data,<br/>Chris Ding, and Hanchuan Peng, Journal of Bioinformatics and Computational Biology, Vol. 3, No. 2, pp.185-205, 2005.<br/><br/>Hanchuan Peng's mRMR Homepage at <a target=\"_blank\" href=\"http://penglab.janelia.org/proj/mRMR/\">http://penglab.janelia.org/proj/mRMR/</a><br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.12"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.12.html"}]}, {"id": "SAGA.table_calculus.6", "title": "Cluster Analysis", "description": "Cluster Analysis for tables.<br/><br/>References:<br/><br/>Iterative Minimum Distance:<br/>- Forgy, E. (1965):<br/> 'Cluster Analysis of multivariate data: efficiency vs. interpretability of classifications',<br/> Biometrics 21:768<br/><br/>Hill-Climbing:- Rubin, J. (1967):<br/> 'Optimal Classification into Groups: An Approach for Solving the Taxonomy Problem',<br/> J. Theoretical Biology, 15:103-144<br/><br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.6"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.6.html"}]}, {"id": "SAGA.table_calculus.5", "title": "Running Average", "description": "Running Average", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.5"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.5.html"}]}, {"id": "SAGA.table_calculus.1", "title": "Field Calculator", "description": "The table calculator calculates a new attribute from existing attributes based on a mathematical formula. Attributes are addressed by the character 'f' (for 'field') followed by the field number (i.e.: f1, f2, ..., fn) or by the field name in square brackets (e.g.: [Field Name]).<br/>Examples:<br/>- sin(f1) * f2 + f3<br/>- [Population] / [Area]<br/><br/>If the use no-data flag is unchecked and a no-data value appears in a record's input, no calculation is performed for it and the result is set to no-data.<br/><br/>Following operators are available for the formula definition:<br/><table border=\"0\"><tr><td><b>+</b></td><td>Addition</td></tr><tr><td><b>-</b></td><td>Subtraction</td></tr><tr><td><b>*</b></td><td>Multiplication</td></tr><tr><td><b>/</b></td><td>Division</td></tr><tr><td><b>abs(x)</b></td><td>Absolute Value</td></tr><tr><td><b>mod(x, y)</b></td><td>Returns the floating point remainder of x/y</td></tr><tr><td><b>int(x)</b></td><td>Returns the integer part of floating point value x</td></tr><tr><td><b>sqr(x)</b></td><td>Square</td></tr><tr><td><b>sqrt(x)</b></td><td>Square Root</td></tr><tr><td><b>exp(x)</b></td><td>Exponential</td></tr><tr><td><b>pow(x, y)</b></td><td>Returns x raised to the power of y</td></tr><tr><td><b>x ^ y</b></td><td>Returns x raised to the power of y</td></tr><tr><td><b>ln(x)</b></td><td>Natural Logarithm</td></tr><tr><td><b>log(x)</b></td><td>Base 10 Logarithm</td></tr><tr><td><b>pi()</b></td><td>Returns the value of Pi</td></tr><tr><td><b>sin(x)</b></td><td>Sine</td></tr><tr><td><b>cos(x)</b></td><td>Cosine</td></tr><tr><td><b>tan(x)</b></td><td>Tangent</td></tr><tr><td><b>asin(x)</b></td><td>Arcsine</td></tr><tr><td><b>acos(x)</b></td><td>Arccosine</td></tr><tr><td><b>atan(x)</b></td><td>Arctangent</td></tr><tr><td><b>atan2(x, y)</b></td><td>Arctangent of x/y</td></tr><tr><td><b>min(x, y)</b></td><td>Returns the minimum of values x and y</td></tr><tr><td><b>max(x, y)</b></td><td>Returns the maximum of values x and y</td></tr><tr><td><b>gt(x, y)</b></td><td>Returns true (1), if x is greater than y, else false (0)</td></tr><tr><td><b>x > y</b></td><td>Returns true (1), if x is greater than y, else false (0)</td></tr><tr><td><b>lt(x, y)</b></td><td>Returns true (1), if x is less than y, else false (0)</td></tr><tr><td><b>x < y</b></td><td>Returns true (1), if x is less than y, else false (0)</td></tr><tr><td><b>eq(x, y)</b></td><td>Returns true (1), if x equals y, else false (0)</td></tr><tr><td><b>x = y</b></td><td>Returns true (1), if x equals y, else false (0)</td></tr><tr><td><b>and(x, y)</b></td><td>Returns true (1), if both x and y are true (i.e. not 0)</td></tr><tr><td><b>or(x, y)</b></td><td>Returns true (1), if at least one of both x and y is true (i.e. not 0)</td></tr><tr><td><b>ifelse(c, x, y)</b></td><td>Returns x, if condition c is true (i.e. not 0), else y</td></tr><tr><td><b>rand_u(x, y)</b></td><td>Random number, uniform distribution with minimum x and maximum y</td></tr><tr><td><b>rand_g(x, y)</b></td><td>Random number, Gaussian distribution with mean x and standard deviation y</td></tr><tr><td><b>nodata()</b></td><td>Returns tables's no-data value</td></tr><tr><td><b>isnodata(x)</b></td><td>Returns true (1), if x is a no-data value, else false (0)</td></tr></table>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.1"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.1.html"}]}, {"id": "SAGA.table_calculus.2", "title": "Field Calculator [Shapes]", "description": "The table calculator calculates a new attribute from existing attributes based on a mathematical formula. Attributes are addressed by the character 'f' (for 'field') followed by the field number (i.e.: f1, f2, ..., fn) or by the field name in square brackets (e.g.: [Field Name]).<br/>Examples:<br/>- sin(f1) * f2 + f3<br/>- [Population] / [Area]<br/><br/>If the use no-data flag is unchecked and a no-data value appears in a record's input, no calculation is performed for it and the result is set to no-data.<br/><br/>Following operators are available for the formula definition:<br/><table border=\"0\"><tr><td><b>+</b></td><td>Addition</td></tr><tr><td><b>-</b></td><td>Subtraction</td></tr><tr><td><b>*</b></td><td>Multiplication</td></tr><tr><td><b>/</b></td><td>Division</td></tr><tr><td><b>abs(x)</b></td><td>Absolute Value</td></tr><tr><td><b>mod(x, y)</b></td><td>Returns the floating point remainder of x/y</td></tr><tr><td><b>int(x)</b></td><td>Returns the integer part of floating point value x</td></tr><tr><td><b>sqr(x)</b></td><td>Square</td></tr><tr><td><b>sqrt(x)</b></td><td>Square Root</td></tr><tr><td><b>exp(x)</b></td><td>Exponential</td></tr><tr><td><b>pow(x, y)</b></td><td>Returns x raised to the power of y</td></tr><tr><td><b>x ^ y</b></td><td>Returns x raised to the power of y</td></tr><tr><td><b>ln(x)</b></td><td>Natural Logarithm</td></tr><tr><td><b>log(x)</b></td><td>Base 10 Logarithm</td></tr><tr><td><b>pi()</b></td><td>Returns the value of Pi</td></tr><tr><td><b>sin(x)</b></td><td>Sine</td></tr><tr><td><b>cos(x)</b></td><td>Cosine</td></tr><tr><td><b>tan(x)</b></td><td>Tangent</td></tr><tr><td><b>asin(x)</b></td><td>Arcsine</td></tr><tr><td><b>acos(x)</b></td><td>Arccosine</td></tr><tr><td><b>atan(x)</b></td><td>Arctangent</td></tr><tr><td><b>atan2(x, y)</b></td><td>Arctangent of x/y</td></tr><tr><td><b>min(x, y)</b></td><td>Returns the minimum of values x and y</td></tr><tr><td><b>max(x, y)</b></td><td>Returns the maximum of values x and y</td></tr><tr><td><b>gt(x, y)</b></td><td>Returns true (1), if x is greater than y, else false (0)</td></tr><tr><td><b>x > y</b></td><td>Returns true (1), if x is greater than y, else false (0)</td></tr><tr><td><b>lt(x, y)</b></td><td>Returns true (1), if x is less than y, else false (0)</td></tr><tr><td><b>x < y</b></td><td>Returns true (1), if x is less than y, else false (0)</td></tr><tr><td><b>eq(x, y)</b></td><td>Returns true (1), if x equals y, else false (0)</td></tr><tr><td><b>x = y</b></td><td>Returns true (1), if x equals y, else false (0)</td></tr><tr><td><b>and(x, y)</b></td><td>Returns true (1), if both x and y are true (i.e. not 0)</td></tr><tr><td><b>or(x, y)</b></td><td>Returns true (1), if at least one of both x and y is true (i.e. not 0)</td></tr><tr><td><b>ifelse(c, x, y)</b></td><td>Returns x, if condition c is true (i.e. not 0), else y</td></tr><tr><td><b>rand_u(x, y)</b></td><td>Random number, uniform distribution with minimum x and maximum y</td></tr><tr><td><b>rand_g(x, y)</b></td><td>Random number, Gaussian distribution with mean x and standard deviation y</td></tr><tr><td><b>nodata()</b></td><td>Returns tables's no-data value</td></tr><tr><td><b>isnodata(x)</b></td><td>Returns true (1), if x is a no-data value, else false (0)</td></tr></table>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.2"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.2.html"}]}, {"id": "SAGA.table_calculus.16", "title": "Record Statistics", "description": "This tool calculates record-wise the statistics over the selected attribute fields.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.16"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.16.html"}]}, {"id": "SAGA.table_calculus.14", "title": "Cluster Analysis (Shapes)", "description": "Cluster Analysis for tables.<br/><br/>References:<br/><br/>Iterative Minimum Distance:<br/>- Forgy, E. (1965):<br/> 'Cluster Analysis of multivariate data: efficiency vs. interpretability of classifications',<br/> Biometrics 21:768<br/><br/>Hill-Climbing:- Rubin, J. (1967):<br/> 'Optimal Classification into Groups: An Approach for Solving the Taxonomy Problem',<br/> J. Theoretical Biology, 15:103-144<br/><br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.14"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.14.html"}]}, {"id": "SAGA.table_calculus.8", "title": "Fill Gaps in Ordered Records", "description": "This tool inserts records where the chosen order field has gaps expecting an increment of one. It is assumed that the order field represents integers.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.8"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.8.html"}]}, {"id": "SAGA.table_calculus.11", "title": "Find Field of Extreme Value", "description": "Identifies from the selected attributes that one, which has the maximum or respectively the minimum value. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.11"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.11.html"}]}, {"id": "SAGA.table_calculus.9", "title": "Fill Gaps in Records", "description": "This tool fills gaps in the table records. for the chosen attribute field it interpolates values for those records, which have no-data, using existing data from the surrounding records. If no order field is specified, simply the record index is taken as coordinate, for which the interpolation will be performed. Notice: extrapolation is not supported, i.e. only those gaps will be filled that have lower and higher values surrounding them following the record order. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.9"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.9.html"}]}, {"id": "SAGA.table_calculus.15", "title": "Field Statistics", "description": "The tools allows one to calculate statistics (n, min, max, range, sum, mean, variance and standard deviation) for attribute fields of tables, shapefiles or point clouds.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.15"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.15.html"}]}, {"id": "SAGA.table_calculus.17", "title": "Record Statistics (Shapes)", "description": "This tool calculates record-wise the statistics over the selected attribute fields.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.17"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_calculus.17.html"}]}, {"id": "SAGA.imagery_classification.6", "title": "Confusion Matrix (Polygons / Grid)", "description": "Compares a classified polygons layer with grid classes and creates a confusion matrix and derived coefficients. Grid classes have to be defined with a look-up table and values must match those of the polygon classes for the subsequent comparison. This tool is typically used for a quality assessment of a supervised classification. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_classification.6"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_classification.6.html"}]}, {"id": "SAGA.imagery_classification.5", "title": "Supervised Classification for Tables", "description": "Supervised classification for attribute data. To train the classifier choose an attribute that provides class identifiers for those records, for which the target class is known, and no data for all other records.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_classification.5"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_classification.5.html"}]}, {"id": "SAGA.imagery_classification.1", "title": "K-Means Clustering for Grids", "description": "This tool implements the K-Means cluster analysis for grids in two variants, iterative minimum distance (Forgy 1965) and hill climbing (Rubin 1967). ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_classification.1"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_classification.1.html"}]}, {"id": "SAGA.imagery_classification.3", "title": "Decision Tree", "description": "Decision Tree", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_classification.3"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_classification.3.html"}]}, {"id": "SAGA.imagery_classification.2", "title": "Confusion Matrix (Two Grids)", "description": "Compares two classified grids and creates a confusion matrix and derived coefficients as well as the combinations of both classifications as new grid. Grid classes have to be defined with a look-up table and values of both grids must match each other for the subsequent comparison. A typical application is a change detection analysis based on land cover classification of satellite imagery. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_classification.2"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_classification.2.html"}]}, {"id": "SAGA.imagery_classification.0", "title": "Supervised Classification for Grids", "description": "Supervised Classification for Grids", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_classification.0"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_classification.0.html"}]}, {"id": "SAGA.imagery_classification.4", "title": "Supervised Classification for Shapes", "description": "Supervised classification for attribute data. To train the classifier choose an attribute that provides class identifiers for those records, for which the target class is known, and no data for all other records.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_classification.4"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_classification.4.html"}]}, {"id": "SAGA.garden_fractals.5", "title": "Gaussian Landscapes", "description": "Generates Gaussian landscapes.<br/><br/>References:<br/>- Halling, H., Moeller, R. (1995): 'Mathematik fuers Auge', Heidelberg, 144p.<br/>- Mandelbrot, B.B. (1983): 'The Fractal Geometry of Nature', New York, 490p.<br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.garden_fractals.5"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.garden_fractals.5.html"}]}, {"id": "SAGA.garden_fractals.1", "title": "Pythagoras' Tree", "description": "Pythagoras' Tree.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.garden_fractals.1"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.garden_fractals.1.html"}]}, {"id": "SAGA.garden_fractals.3", "title": "Fractal Dimension of Grid Surface", "description": "Calculates surface areas for increasing mesh sizes.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.garden_fractals.3"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.garden_fractals.3.html"}]}, {"id": "SAGA.garden_fractals.0", "title": "Bifurcation", "description": "Feigenbaum's Bifurcation", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.garden_fractals.0"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.garden_fractals.0.html"}]}, {"id": "SAGA.ta_hydrology.18", "title": "Flow Accumulation (Mass-Flux Method)", "description": "The Mass-Flux Method (MFM) for the DEM based calculation of flow accumulation as proposed by Gruber and Peckham (2008).<br/><br/>!!!UNDER DEVELOPMENT!!! To be done: solving the streamline resolution problem", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.18"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.18.html"}]}, {"id": "SAGA.ta_hydrology.10", "title": "Cell Balance", "description": "Cell Balance", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.10"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.10.html"}]}, {"id": "SAGA.ta_hydrology.7", "title": "Slope Length", "description": "Slope Length", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.7"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.7.html"}]}, {"id": "SAGA.ta_hydrology.6", "title": "Flow Path Length", "description": "This tool calculates the average flow path length starting from the seeds, that are given by the optional 'Seeds' grid and optionally from cells without upslope contributing areas (i.e. summits, ridges). Seeds will be all grid cells, that are not 'no data' values. If seeds are not given, only summits and ridges as given by the flow routing will be taken into account. Available flow routing methods are based on the 'Deterministic 8 (D8)' (Callaghan and Mark 1984) and the 'Multiple Flow Direction (FD8)' (Freeman 1991, Quinn et al. 1991) algorithms.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.6"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.6.html"}]}, {"id": "SAGA.ta_hydrology.20", "title": "Topographic Wetness Index (TWI)", "description": "Calculation of the slope and specific catchment area (SCA) based Topographic Wetness Index (TWI).", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.20"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.20.html"}]}, {"id": "SAGA.ta_hydrology.1", "title": "Flow Accumulation (Recursive)", "description": "Recursive upward processing of cells for calculation of flow accumulation and related parameters. This set of algorithms processes recursively all upwards connected cells until each cell of the DEM has been processed.<br/><br/>Flow routing methods provided by this tool:<ul><li>Deterministic 8 (aka D8, O'Callaghan & Mark 1984)</li><li>Rho 8 (Fairfield & Leymarie 1991)</li><li>Multiple Flow Direction (Freeman 1991, Quinn et al. 1991)</li><li>Deterministic Infinity (Tarboton 1997)</li></ul>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.1"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.1.html"}]}, {"id": "SAGA.ta_hydrology.29", "title": "Flow Accumulation (Parallelizable)", "description": "A simple implementation of a parallelizable flow accumulation algorithn.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.29"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.29.html"}]}, {"id": "SAGA.ta_hydrology.26", "title": "Slope Limited Flow Accumulation", "description": "Flow accumulation is calculated as upslope contributing (catchment) area using the multiple flow direction approach of Freeman (1991). For this tool the approach has been modified to limit the flow portion routed through a cell depending on the local slope. If a cell is not inclined, no flow is routed through it at all. With increasing slopes the portion of flow routed through a cell becomes higher. Cells with slopes greater than a specified slope threshold route their entire accumulated flow downhill. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.26"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.26.html"}]}, {"id": "SAGA.ta_hydrology.21", "title": "Stream Power Index", "description": "Calculation of stream power index based on slope and specific catchment area (SCA).<br/>SPI = SCA * tan(Slope)", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.21"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.21.html"}]}, {"id": "SAGA.ta_hydrology.2", "title": "Flow Accumulation (Flow Tracing)", "description": "Flow tracing algorithms for calculations of flow accumulation and related parameters. These algorithms trace the flow of each cell in a DEM separately until it finally leaves the DEM or ends in a sink.<br/>The Rho 8 implementation (Fairfield & Leymarie 1991) adopts the original algorithm only for the flow routing and will give quite different results.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.2"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.2.html"}]}, {"id": "SAGA.ta_hydrology.25", "title": "LS-Factor, Field Based", "description": "Calculation of slope length (LS) factor as used for the Universal Soil Loss Equation (USLE), based on slope and (specific) catchment area, latter as substitute for slope length. This tool takes only a Digital Elevation Model (DEM) as input and derives catchment areas according to Freeman (1991). Optionally field polygons can be supplied. Is this the case, calculations will be performed field by field, i.e. catchment area calculation is restricted to each field's area.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.25"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.25.html"}]}, {"id": "SAGA.ta_hydrology.19", "title": "Flow Width and Specific Catchment Area", "description": "Flow width and specific catchment area (SCA) calculation. SCA calculation needs total catchment area (TCA) as input, which can be calculated with one of the flow accumulation tools. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.19"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.19.html"}]}, {"id": "SAGA.ta_hydrology.27", "title": "Maximum Flow Path Length", "description": "This tool calculates the maximum upstream or downstream distance or weighted distance along the flow path for each cell based on 'Deterministic 8 (D8)' (O'Callaghan and Mark 1984) flow directions.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.27"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.27.html"}]}, {"id": "SAGA.ta_hydrology.16", "title": "Lake Flood", "description": "This tool can be used to flood a digital elevation model from seed points. Seed points have to be coded either with local water depth or absolute water level.<br/><br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.16"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.16.html"}]}, {"id": "SAGA.ta_hydrology.0", "title": "Flow Accumulation (Top-Down)", "description": "Top-down processing of cells for calculation of flow accumulation and related parameters. This set of algorithms processes a DEM downwards from the highest to the lowest cell.<br/><br/>Flow routing methods provided by this tool:<ul><li>Deterministic 8 (aka D8, O'Callaghan & Mark 1984)</li><li>Braunschweiger Reliefmodell (Bauer et al. 1985)</li><li>Rho 8 (Fairfield & Leymarie 1991)</li><li>Multiple Flow Direction (Freeman 1991, Quinn et al. 1991)</li><li>Deterministic Infinity (Tarboton 1997)</li><li>Triangular Multiple Flow Direction (Seibert & McGlynn 2007</li><li>Multiple Flow Direction based on Maximum Downslope Gradient (Qin et al. 2011)</li></ul>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.0"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.0.html"}]}, {"id": "SAGA.ta_hydrology.22", "title": "LS Factor", "description": "Calculation of slope length (LS) factor as used by the Universal Soil Loss Equation (USLE), based on slope and specific catchment area (SCA, as substitute for slope length).", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.22"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.22.html"}]}, {"id": "SAGA.ta_hydrology.4", "title": "Upslope Area", "description": "This tool allows you to specify target cells, for which the upslope contributing area shall be identified. The result will give for each cell the percentage of its flow that reaches the target cell(s).<br/>_______<br/><br/>This version uses all valid cells (not 'no data' values) of a given target grid to determine the contributing area. In case no target grid is provided as input, the specified x/y coordinates are used as target point.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.4"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.4.html"}]}, {"id": "SAGA.ta_hydrology.24", "title": "TCI Low", "description": "Terrain Classification Index for Lowlands (TCI Low).", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.24"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.24.html"}]}, {"id": "SAGA.ta_hydrology.15", "title": "SAGA Wetness Index", "description": "The 'SAGA Wetness Index' is, as the name says, similar to the 'Topographic Wetness Index' (TWI), but it is based on a modified catchment area calculation ('Modified Catchment Area'), which does not think of the flow as very thin film. As result it predicts for cells situated in valley floors with a small vertical distance to a channel a more realistic, higher potential soil moisture compared to the standard TWI calculation.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.15"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.15.html"}]}, {"id": "SAGA.ta_hydrology.28", "title": "Flow between fields", "description": "Flow between fields (identified by ID)", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.28"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.28.html"}]}, {"id": "SAGA.ta_hydrology.13", "title": "Edge Contamination", "description": "This tool uses flow directions to estimate possible contamination effects moving from outside of the grid passing the edge into its interior. This means that derived contributing area values might be underestimated for the marked cells. Cells not contamined will be marked as no data. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.13"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.13.html"}]}, {"id": "SAGA.ta_hydrology.23", "title": "Melton Ruggedness Number", "description": "Melton ruggedness number (MNR) is a simple flow accumulation related index, calculated as difference between maximum and minimum elevation in catchment area divided by square root of catchment area size. The calculation is performed for each grid cell, therefore minimum elevation is same as elevation at cell's position. Due to the discrete character of a single maximum elevation, flow calculation is simply done with Deterministic 8.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.23"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.ta_hydrology.23.html"}]}, {"id": "SAGA.sim_rivflow.1", "title": "LandFlow Version 1.0 (build 3.5.1b)", "description": "LandFlow Version 1.0 (build 3.5.1b)", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_rivflow.1"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_rivflow.1.html"}]}, {"id": "SAGA.sim_rivflow.3", "title": "RiverGridGeneration", "description": "Generation of RiverCourse-GridCells", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_rivflow.3"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_rivflow.3.html"}]}, {"id": "SAGA.sim_rivflow.0", "title": "RiverBasin", "description": "Parameters of RiverBasin", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_rivflow.0"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_rivflow.0.html"}]}, {"id": "SAGA.imagery_svm.0", "title": "SVM Classification", "description": "Support Vector Machine (SVM) based classification for grids.<br/>Reference:<br/>Chang, C.-C. / Lin, C.-J. (2011): A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, vol.2/3, p.1-27. <a target=\"_blank\" href=\"http://www.csie.ntu.edu.tw/~cjlin/libsvm\">LIBSVM Homepage</a>.<br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_svm.0"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.imagery_svm.0.html"}]}, {"id": "SAGA.sim_qm_of_esp.1", "title": "Fill Sinks (QM of ESP)", "description": "Filling in pits and flats in a DEM.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_qm_of_esp.1"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_qm_of_esp.1.html"}]}, {"id": "SAGA.sim_qm_of_esp.3", "title": "Successive Flow Routing", "description": "Calculation of flow accumulation, aka upslope contributing area, with the multiple flow direction method after Freeman (1991).", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_qm_of_esp.3"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_qm_of_esp.3.html"}]}, {"id": "SAGA.sim_qm_of_esp.2", "title": "Flow Accumulation (QM of ESP)", "description": "Calculation of flow accumulation, aka upslope contributing area, with the multiple flow direction method after Freeman (1991).", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_qm_of_esp.2"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_qm_of_esp.2.html"}]}, {"id": "SAGA.sim_qm_of_esp.0", "title": "Diffusive Hillslope Evolution (FTCS)", "description": "Simulation of diffusive hillslope evolution using a Forward-Time-Centered-Space (FTCS) method.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_qm_of_esp.0"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_qm_of_esp.0.html"}]}, {"id": "SAGA.sim_qm_of_esp.4", "title": "Diffusive Hillslope Evolution (ADI)", "description": "Simulation of diffusive hillslope evolution using an Alternating-Direction-Implicit (ADI) method.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_qm_of_esp.4"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.sim_qm_of_esp.4.html"}]}, {"id": "SAGA.grid_spline.7", "title": "Multilevel B-Spline for Categories", "description": "The 'Multilevel B-Spline for Categories' tool is comparable to indicator Kriging except that uses the Multilevel B-spline algorithm for interpolation. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.7"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.7.html"}]}, {"id": "SAGA.grid_spline.6", "title": "Cubic Spline Approximation", "description": "This tool approximates irregular scalar 2D data in specified points using C1-continuous bivariate cubic spline.<br/>Minimal Number of Points: minimal number of points locally involved in spline calculation (normally = 3)<br/><br/>Maximal Number of Points:npmax: maximal number of points locally involved in spline calculation (required > 10, recommended 20 < npmax < 60)<br/>Tolerance: relative tolerance multiple in fitting spline coefficients: the higher this value, the higher degree of the locally fitted spline (recommended 80 < k < 200)<br/><br/>Points per square: average number of points per square (increase if the point distribution is strongly non-uniform to get larger cells)<br/><br/>Author: Pavel Sakov, CSIRO Marine Research<br/><br/>Purpose: 2D data approximation with bivariate C1 cubic spline. A set of library functions + standalone utility.<br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.6"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.6.html"}]}, {"id": "SAGA.grid_spline.5", "title": "Multilevel B-Spline from Grid Points", "description": "Multilevel B-spline algorithm for spatial interpolation of scattered data as proposed by Lee, Wolberg and Shin (1997). The algorithm makes use of a coarse-to-fine hierarchy of control lattices to generate a sequence of bicubic B-spline functions, whose sum approaches the desired interpolation function. Large performance gains are realized by using B-spline refinement to reduce the sum of these functions into one equivalent B-spline function. <br/><br/>The 'Maximum Level' determines the maximum size of the final B-spline matrix and increases exponential with each level. Where level=10 requires about 1mb level=12 needs about 16mb and level=14 about 256mb(!) of additional memory. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.5"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.5.html"}]}, {"id": "SAGA.grid_spline.1", "title": "Thin Plate Spline", "description": "Creates a 'Thin Plate Spline' function for each grid point based on all of the scattered data points that are within a given distance. The number of points can be limited to a maximum number of closest points. <br/><br/>References:<br/>- Donato G., Belongie S. (2002): 'Approximation Methods for Thin Plate Spline Mappings and Principal Warps', In Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (Eds.): 'Computer Vision - ECCV 2002: 7th European Conference on Computer Vision, Copenhagen, Denmark, May 28-31, 2002', Proceedings, Part III, Lecture Notes in Computer Science. Springer-Verlag Heidelberg; pp.21-31.<br/><br/>- Elonen, J. (2005): 'Thin Plate Spline editor - an example program in C++', <a target=\"_blank\" href=\"http://elonen.iki.fi/code/tpsdemo/index.html\">http://elonen.iki.fi/code/tpsdemo/index.html</a>.<br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.1"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.1.html"}]}, {"id": "SAGA.grid_spline.3", "title": "B-Spline Approximation", "description": "Calculates B-spline functions for chosen level of detail. This tool serves as the basis for the 'Multilevel B-spline Interpolation' and is not suited as is for spatial data interpolation from scattered data. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.3"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.3.html"}]}, {"id": "SAGA.grid_spline.2", "title": "Thin Plate Spline (TIN)", "description": "Creates a 'Thin Plate Spline' function for each triangle of a TIN and uses it for subsequent gridding. The TIN is internally created from the scattered data points input. The 'Neighbourhood' option determines the number of points used for the spline generation. 'Immediate neighbourhood' includes the points of the triangle as well as the immediate neighbour points. 'Level 1' adds the neighbours of the immediate neighbourhood and 'level 2' adds the neighbours of 'level 1' neighbours too. A higher neighbourhood degree reduces sharp breaks but also increases the computation time. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.2"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.2.html"}]}, {"id": "SAGA.grid_spline.8", "title": "Multilevel B-Spline (3D)", "description": "Multilevel B-spline algorithm for spatial interpolation of scattered data as proposed by Lee, Wolberg and Shin (1997) modified for 3D data.<br/>The algorithm makes use of a coarse-to-fine hierarchy of control lattices to generate a sequence of bicubic B-spline functions, whose sum approaches the desired interpolation function. Performance gains are realized by using B-spline refinement to reduce the sum of these functions into one equivalent B-spline function. <br/><br/>The 'Maximum Level' determines the maximum size of the final B-spline matrix and increases exponential with each level. Where level=10 requires about 1mb level=12 needs about 16mb and level=14 about 256mb(!) of additional memory. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.8"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.8.html"}]}, {"id": "SAGA.grid_spline.4", "title": "Multilevel B-Spline", "description": "Multilevel B-spline algorithm for spatial interpolation of scattered data as proposed by Lee, Wolberg and Shin (1997).<br/>The algorithm makes use of a coarse-to-fine hierarchy of control lattices to generate a sequence of bicubic B-spline functions, whose sum approaches the desired interpolation function. Performance gains are realized by using B-spline refinement to reduce the sum of these functions into one equivalent B-spline function. <br/><br/>The 'Maximum Level' determines the maximum size of the final B-spline matrix and increases exponential with each level. Where level=10 requires about 1mb level=12 needs about 16mb and level=14 about 256mb(!) of additional memory. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.4"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.grid_spline.4.html"}]}, {"id": "SAGA.table_tools.10", "title": "Replace Text", "description": "For the selected attribute or, if not specified, for all text attributes this tool replaces text strings with replacements as defined in table 'Text Replacements'.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.10"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.10.html"}]}, {"id": "SAGA.table_tools.7", "title": "Change Field Type", "description": "With this tool you can change the data type of a table's attribute field.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.7"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.7.html"}]}, {"id": "SAGA.table_tools.6", "title": "Change Time Format", "description": "Change Time Format", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.6"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.6.html"}]}, {"id": "SAGA.table_tools.20", "title": "Add Indicator Fields for Categories", "description": "Adds for each unique value found in the category field an indicator field that will show a value of one (1) for all records with this category value and zero (0) for all others. This might be used e.g. for subsequent indicator kriging. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.20"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.20.html"}]}, {"id": "SAGA.table_tools.5", "title": "Change Date Format", "description": "Change Date Format", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.5"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.5.html"}]}, {"id": "SAGA.table_tools.1", "title": "Transpose Table", "description": "Transposes a table, i.e. to swap rows and columns.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.1"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.1.html"}]}, {"id": "SAGA.table_tools.3", "title": "Join Attributes from a Table", "description": "Joins two tables using key attributes.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.3"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.3.html"}]}, {"id": "SAGA.table_tools.21", "title": "Table Field Enumeration (Shapes)", "description": "Enumeration of a table attribute, i.e. a unique identifier is assigned to identical values of the chosen attribute field. If no attribute is chosen, a simple enumeration is done for all records, and this with respect to the sorting order if the dataset has been indexed.<br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.21"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.21.html"}]}, {"id": "SAGA.table_tools.2", "title": "Table Field Enumeration", "description": "Enumeration of a table attribute, i.e. a unique identifier is assigned to identical values of the chosen attribute field. If no attribute is chosen, a simple enumeration is done for all records, and this with respect to the sorting order if the dataset has been indexed.<br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.2"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.2.html"}]}, {"id": "SAGA.table_tools.25", "title": "Formatted Text [Shapes]", "description": "With this tool you can create new text field contents from the contents of other fields. To address other field's contents you have some format options as listed below.<br/>Fields are addressed either by their zero based column number or by their name.<br/>If the use <i>no-data flag</i> is unchecked and a no-data value appears in a record's input fields, the result will be an empty text string.<br/>Field contents can be combined using the '+' operator. Free text arguments have to be added in quota.<br/>A simple example:<br/><i>\"No. \" + index(1) + \": the value of '\" + upper(0) + \"' is \" + number(1, 2)</i><br/><table border=\"0\"><tr><td><b>index(offset = 0)</b></td><td>record's index</td></tr><tr><td><b>string(field)</b></td><td>field's content as it is</td></tr><tr><td><b>lower(field)</b></td><td>field's content as lower case text</td></tr><tr><td><b>upper(field)</b></td><td>field's content as upper case text</td></tr><tr><td><b>integer(field)</b></td><td>field's content as integer number</td></tr><tr><td><b>real(field, precision)</b></td><td>field's content as real number with optional precision argument</td></tr></table>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.25"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.25.html"}]}, {"id": "SAGA.table_tools.8", "title": "Append Fields from another Table", "description": "Append Fields from another Table", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.8"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.8.html"}]}, {"id": "SAGA.table_tools.0", "title": "Create New Table", "description": "Creates a new empty table.<br/><br/>Possible field types are:<br/>- string<br/>- date<br/>- color<br/>- unsigned 1 byte integer<br/>- signed 1 byte integer<br/>- unsigned 2 byte integer<br/>- signed 2 byte integer<br/>- unsigned 4 byte integer<br/>- signed 4 byte integer<br/>- unsigned 8 byte integer<br/>- signed 8 byte integer<br/>- 4 byte floating point number<br/>- 8 byte floating point number<br/>- binary<br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.0"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.0.html"}]}, {"id": "SAGA.table_tools.22", "title": "Copy Table", "description": "Creates a copy of a table.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.22"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.22.html"}]}, {"id": "SAGA.table_tools.11", "title": "Delete Fields", "description": "Deletes selected fields from a table or shapefile. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.11"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.11.html"}]}, {"id": "SAGA.table_tools.4", "title": "Join Attributes from a Table (Shapes)", "description": "Joins two tables using key attributes.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.4"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.4.html"}]}, {"id": "SAGA.table_tools.24", "title": "Formatted Text", "description": "With this tool you can create new text field contents from the contents of other fields. To address other field's contents you have some format options as listed below.<br/>Fields are addressed either by their zero based column number or by their name.<br/>If the use <i>no-data flag</i> is unchecked and a no-data value appears in a record's input fields, the result will be an empty text string.<br/>Field contents can be combined using the '+' operator. Free text arguments have to be added in quota.<br/>A simple example:<br/><i>\"No. \" + index(1) + \": the value of '\" + upper(0) + \"' is \" + number(1, 2)</i><br/><table border=\"0\"><tr><td><b>index(offset = 0)</b></td><td>record's index</td></tr><tr><td><b>string(field)</b></td><td>field's content as it is</td></tr><tr><td><b>lower(field)</b></td><td>field's content as lower case text</td></tr><tr><td><b>upper(field)</b></td><td>field's content as upper case text</td></tr><tr><td><b>integer(field)</b></td><td>field's content as integer number</td></tr><tr><td><b>real(field, precision)</b></td><td>field's content as real number with optional precision argument</td></tr></table>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.24"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.24.html"}]}, {"id": "SAGA.table_tools.9", "title": "Change Color Format", "description": "This tool allows one to convert table fields from SAGA RGB to R, G, B values and vice versa.<br/><br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.9"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.9.html"}]}, {"id": "SAGA.table_tools.15", "title": "Copy Selection", "description": "Copies selected records to a new table.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.15"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.15.html"}]}, {"id": "SAGA.table_tools.23", "title": "Change Field Name", "description": "With this tool you can change the name of a table's attribute field.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.23"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.table_tools.23.html"}]}, {"id": "SAGA.pointcloud_tools.10", "title": "Point Cloud Attribute Calculator", "description": "The Point Cloud Attribute Calculator calculates a new attribute based on existing attributes and a mathematical formula. Attribute fields are addressed by the character 'f' (for 'field') followed by the field number (i.e.: f1, f2, ..., fn) or by the field name in square brackets (e.g.: [Field Name]).<br/>Examples:<br/>sin(f1) * f2 + f3<br/>[intensity] / 1000<br/><br/>The following operators are available for the formula definition:<br/><table border=\"0\"><tr><td><b>+</b></td><td>Addition</td></tr><tr><td><b>-</b></td><td>Subtraction</td></tr><tr><td><b>*</b></td><td>Multiplication</td></tr><tr><td><b>/</b></td><td>Division</td></tr><tr><td><b>abs(x)</b></td><td>Absolute Value</td></tr><tr><td><b>mod(x, y)</b></td><td>Returns the floating point remainder of x/y</td></tr><tr><td><b>int(x)</b></td><td>Returns the integer part of floating point value x</td></tr><tr><td><b>sqr(x)</b></td><td>Square</td></tr><tr><td><b>sqrt(x)</b></td><td>Square Root</td></tr><tr><td><b>exp(x)</b></td><td>Exponential</td></tr><tr><td><b>pow(x, y)</b></td><td>Returns x raised to the power of y</td></tr><tr><td><b>x ^ y</b></td><td>Returns x raised to the power of y</td></tr><tr><td><b>ln(x)</b></td><td>Natural Logarithm</td></tr><tr><td><b>log(x)</b></td><td>Base 10 Logarithm</td></tr><tr><td><b>pi()</b></td><td>Returns the value of Pi</td></tr><tr><td><b>sin(x)</b></td><td>Sine</td></tr><tr><td><b>cos(x)</b></td><td>Cosine</td></tr><tr><td><b>tan(x)</b></td><td>Tangent</td></tr><tr><td><b>asin(x)</b></td><td>Arcsine</td></tr><tr><td><b>acos(x)</b></td><td>Arccosine</td></tr><tr><td><b>atan(x)</b></td><td>Arctangent</td></tr><tr><td><b>atan2(x, y)</b></td><td>Arctangent of x/y</td></tr><tr><td><b>min(x, y)</b></td><td>Returns the minimum of values x and y</td></tr><tr><td><b>max(x, y)</b></td><td>Returns the maximum of values x and y</td></tr><tr><td><b>gt(x, y)</b></td><td>Returns true (1), if x is greater than y, else false (0)</td></tr><tr><td><b>x > y</b></td><td>Returns true (1), if x is greater than y, else false (0)</td></tr><tr><td><b>lt(x, y)</b></td><td>Returns true (1), if x is less than y, else false (0)</td></tr><tr><td><b>x < y</b></td><td>Returns true (1), if x is less than y, else false (0)</td></tr><tr><td><b>eq(x, y)</b></td><td>Returns true (1), if x equals y, else false (0)</td></tr><tr><td><b>x = y</b></td><td>Returns true (1), if x equals y, else false (0)</td></tr><tr><td><b>and(x, y)</b></td><td>Returns true (1), if both x and y are true (i.e. not 0)</td></tr><tr><td><b>or(x, y)</b></td><td>Returns true (1), if at least one of both x and y is true (i.e. not 0)</td></tr><tr><td><b>ifelse(c, x, y)</b></td><td>Returns x, if condition c is true (i.e. not 0), else y</td></tr><tr><td><b>rand_u(x, y)</b></td><td>Random number, uniform distribution with minimum x and maximum y</td></tr><tr><td><b>rand_g(x, y)</b></td><td>Random number, Gaussian distribution with mean x and standard deviation y</td></tr></table>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.pointcloud_tools.10"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.pointcloud_tools.10.html"}]}, {"id": "SAGA.pointcloud_tools.7", "title": "Drop Point Cloud Attributes", "description": "The tool can be used to drop attributes from a point cloud. In case the output dataset is not set, the attribute(s) will be dropped from the input dataset, i.e. the input dataset will be overwritten.<br/><br/>", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.pointcloud_tools.7"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.pointcloud_tools.7.html"}]}, {"id": "SAGA.pointcloud_tools.12", "title": "Merge Point Clouds", "description": "This tool can be used to merge point clouds. The attribute fields of the merged point cloud resemble those of the first point cloud in the input list. In order to merge the attributes of the additional point cloud layers, these must be consistent (field name and type) with the first point cloud in the input list. Missing attribute values are set to no-data. ", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "self", "type": "application/json", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.pointcloud_tools.12"}, {"rel": "alternate", "type": "text/html", "title": "Process Description", "href": "http://demo.mapmint.com/ogc-api/processes/SAGA.pointcloud_tools.12.html"}]}, {"id": "SAGA.pointcloud_tools.6", "title": "Point Cloud Reclassifier / Subset Extractor", "description": "The tool can be used to either reclassify a Point Cloud attribute or to extract a subset of a Point Cloud based on the values of an attribute.<br/><br/>The tool provides three different options:<br/>(a) reclassification of (or extraction based on) single values,<br/>(b) reclassification of (or extraction based on) a range of values and<br/>(c) reclassification of (or extraction based on) value ranges specified in a lookup table.<br/><br/>Each of these three options provides it's own p