Raster Analysis service tasks

URL:: https://<catalog-url>/System/RasterAnalysisTools/GPServer
Methods:: GET
Operations:: Add Image, Aggregate Multidimensional Raster, Analyze Changes Using CCDC, Analyze Changes Using LandTrendr, Build Multidimensional Transpose, Calculate Density, Calculate Distance, Calculate Travel Cost, Classify, Classify Objects Using Deep Learning, Classify Pixels Using Deep Learning, Compute Accuracy For Object Detection, Convert Feature to Raster, Convert Raster to Feature, Copy Raster, Cost Path as Polyline, Create Image Collection, Create Viewshed, Delete Image, Delete Image Collection, Derive Continuous Flow, Detect Change Using Change Analysis Raster, Detect Objects Using Deep Learning, Determine Optimum Travel Cost Network, Determine Travel Cost Path as Polyline, Determine Travel Cost Paths to Destinations, Distance Accumulation, Distance Allocation, Download Raster, Export Training Data for Deep Learning, Fill, Find Argument Statistics, Flow Accumulation, Flow Direction, Flow Distance, Generate Multidimensional Anomaly, Generate Raster, Generate Trend Raster, Geodesic Viewshed, Install Deep Learning Model, Interpolate Points, Linear Spectral Unmixing, List Deep Learning Model, Locate Regions, Manage Multidimensional Raster, Merge Multidimensional Raster, Nibble, Optimal Path As Line, Optimal Path As Raster, Optimal Region Connections, Predict Using Regression Model, Predict Using Trend Raster, Publish Deep Learning Model, Query Deep Learning Model Info, Sample, Segment, Stream Link, Subset Multidimensional Raster, Summarize Categorical Raster, Summarize Raster Within, Surface Parameters, Tabulate Area, Train Classifier, Train Deep Learning Model, Train Random Trees Regression Model, Uninstall Deep Learning Model, Watershed, Zonal Geometry As Table, Zonal Statistics, Zonal Statistics As Table

Description

The Raster Analysis service contains a number of tasks that you can access and use in your applications. These tasks are arranged below in categories of logical groupings, which do not affect how you access or use the tasks in any way.

Tasks that analyze patterns

The tasks that analyze patterns are described in the following table:

Task	Description
Calculate Density	The `CalculateDensity` task creates a density layer from point features by spreading known quantities of some phenomenon (represented as attributes of the points) across the raster. The result is a layer of areas classified from least dense to most dense.
Compute Change Raster	The `ComputeChangeRaster` task is used to evaluate the difference between two input rasters, and generates a change raster output service. This tool supports continuous raster data and categorical raster data. When the raster inputs are two categorical rasters, the output change raster includes an attribute table containing all the permutations of the from and to classes as well as the pixel counts for all changed and unchanged classes.
Interpolate Points	The `InterpolatePoints` task predicts values at new locations based on measurements from a collection of points. The tool takes point data with values at each point and returns a raster of predicted values.

Tasks that analyze terrain

The tasks that analyze terrain are described in the following table:

Task	Description
Create Viewshed	The `CreateViewshed` task uses an elevation surface and observer locations to identify areas where the observers can see the observed objects and the observed objects can see the observers.
Geodesic Viewshed	The `GeodesicViewshed` task determines the raster surface locations visible to a set of observer features using geodesic methods.
Surface Parameters	The `SurfaceParameters` task determines parameters of a surface raster such as various types of curvatures, slope, and aspect.

Tasks that classify data

The tasks that perform image classification are described in the following table:

Task	Description
Classify	The `Classify` task creates categories of pixels based on the input raster and the classifier definition JSON that was generated from the `TrainClassifier` service.
Linear Spectral Unmixing	The `LinearSpectralUnmixing` task performs subpixel classification and calculates the fractional abundance of end members for individual pixels.
Segment	The `Segment` task groups adjacent pixels that have similar spectral and spatial characteristics into segments.
Train Classifier	The `TrainClassifier` task is a service to train image classifiers in a deep learning model and return an `.ecs` file in JSON format. The `.ecs` file is used in the `Classify` task.
Train Random Trees Regression Model	Models the relationship between explanatory variables (independent variables) and a target dataset (dependent variable).

Tasks that perform deep learning analysis

The tasks that perform deep learning are described in the following table:

Task	Description
Classify Objects Using Deep Learning	The `ClassifyObjectsUsingDeepLearning` task is used to classify objects based on overlaid imagery data using the designated deep learning model and generate a feature service with a new assigned label for each object.
Classify Pixels Using Deep Learning	The `ClassifyPixelsUsingDeepLearning` operation can be used to classify pixels in the imagery data using the designated deep learning model and generate an image service for the classified raster.
Compute Accuracy For Object Detection	The `ComputeAccuracyForObjectDetection` task is used to calculate the accuracy of a deep learning model by comparing the detected objects from the Detect Object Using Deep Learning tool to ground truth data.
Detect Objects Using Deep Learning	The `DetectObjectsUsingDeepLearning` operation can be used to detect objects from the imagery data using the designated deep learning model and generate a feature service for the detected objects.
Export Training Data For Deep Learning	The `ExportTrainingDataforDeepLearning` service generates training sample image chips from the input imagery data with labeled vector data or classified images. The output of this service tool is the data store string where the output image chips, labels, and metadata files will be stored.
Install Deep Learning Model	The `InstallDeepLearningModel` operation is used to install the uploaded deep learning model package (`.dlpk` ) from portal to the raster analysis image server. The upload model package will be unpacked and saved to the server configuration store.
List Deep Learning Models	The `ListDeepLearningModels` operation is used to list all the installed deep learning models on the raster analysis image server.
Publish Deep Learning Model	The `PublishDeepLearningModel` operation publishes a model package of a deep learning model (.dlpk) containing the files and data required to run deep learning inferencing tools for object detection or image classification to your portal as a DLPK item.
Query Deep Learning Model Info	The `QueryDeepLearningModelInfo` operation is used to extract the deep learning model specific settings from the model package item or model definition file.
Train Deep Learning Model	The `TrainDeepLearningModel` task is used to train a deep learning model using the output from the `ExportTrainingDataforDeepLearning` operation. It generates the deep learning model package (`*.dlpk` ) and adds it to an enterprise portal. You can also use this task to write the deep learning model package to a file share data store location.
Uninstall Deep Learning Model	The `UninstallDeepLearningModel` operation is used to uninstall the uploaded deep learning model package (`.dlpk` ) from the raster analysis image server. It will delete the named deep learning model from the image server's configuration store but not the portal item.

Tasks that generalize raster data

The task that generalizes raster data is described in the following table:

Task	Description
Nibble	The `Nibble` task replaces the input cells corresponding to a mask with the values of the nearest neighbors.

Tasks that perform hydrology analysis

The tasks that perform hydrology analysis are described in the following table:

Task	Description
Derive Continuous Flow	The `DeriveContinuousFlow` task generates a raster of accumulated flow into each cell from an input surface raster with no prior sink or depression filling required.
Derive Stream As Line	The `DeriveStreamAsLine` task generates stream line features from an input surface raster with no prior sink or depression filling required.
Derive Stream As Raster	The `DeriveStreamAsRaster` task generates a stream raster from an input surface raster with no prior sink or depression filling required.
Fill	The `Fill` task fills sinks in a surface raster to remove small imperfections in the data.
Flow Accumulation	The `FlowAccumulation` task creates a raster of accumulated flow into each cell. A weight factor can optionally be applied.
Flow Direction	The `FlowDirection` task creates a raster of flow direction from each cell to its steepest downslope neighbor.
Flow Distance	The `FlowDistance` task computes the downslope horizontal or vertical distance to cells in a stream or river into which they flow. A flow direction raster can also be applied. In case of multiple flow paths, minimum, weighted mean, or maximum flow distance can be computed.
Stream Link	The `StreamLink` task assigns unique values to sections of a raster linear network between intersections.
Watershed	The `Watershed` task determines the contributing area above a set of cells in a raster.

Tasks that manage data

The tasks that manage data are described in the following table:

Task	Description
Add Image	The `AddImage` operation allows you to add new images to an existing image collection.
Convert Feature to Raster	The `ConvertFeatureToRaster` task converts a point, line, or polygon feature dataset to a raster.
Convert Raster Function Template	The `ConvertRasterFunctionTemplate` task converts a raster function template between JSON and XML formats. `ConvertRasterFunctionTemplate` takes a raster function template in any of these two formats as input to convert the original source into the other specified format.
Convert Raster to Feature	The `ConvertRasterToFeature` task converts a raster to a point, line, or polygon feature dataset.
Copy Raster	The `CopyRaster` task takes single raster layer input and generates the output image using parallel processing. The input raster dataset can be clipped, resampled, and reprojected based on the setting.
Create Image Collection	The `CreateImageCollection` task takes multiple image items as input, creates a image collection in a registered data store, and publishes it as an image service. The input raster dataset can be clipped, resampled, and reprojected based on the setting. The image upload can also be run in parallel.
Delete Image	The `DeleteImage` task allows you to remove existing images from the image collection. The service will only delete the raster item in the mosaic dataset and will not remove the source image.
Delete Image Collection	The `DeleteImageCollection` task deletes the image collection image service, that is, the portal-hosted image layer item. It will not delete the source images that the image collection references.
Download Raster	The `DownloadRaster` task is used to download an image or partial image at a designated resolution. The input image service must be configured to allow pixel data download.
Generate Raster	The `GenerateRaster` task is a service that allows you to execute raster analysis on a distributed server deployment. The analysis can be specified either with a predefined server raster function keyword or by giving a JSON object representation of a raster function chain.

Tasks that process multidimensional raster data

The tasks that analyze or manage multidimensional raster data are described in the following table:

Task	Description
Aggregate Multidimensional Raster	The `AggregateMultidimensionalRaster` task can be used to generate a `.CRF` multidimensional raster dataset and image service by aggregating existing multidimensional dataset variables along a dimension.
Analyze Changes Using CCDC	The `AnalyzeChangesUsingCCDC` task evaluates changes in pixel values over time using the CCDC algorithm, and generates a multidimensional raster containing the model results.
Analyze Changes Using LandTrendr	The `AnalyzeChangesUsingLandTrendr` task evaluates changes in pixel values over time using the Landsat based detection of trends in disturbance and recovery (LandTrendr) method and generates a change analysis raster containing the model results.
Build Multidimensional Transpose	The `BuildMultidimensionalTranspose` task transposes a multidimensional raster dataset, which divides the multidimensional data along each dimension to optimize performance when accessing pixel values across all slices.
Detect Change Using Change Analysis Raster	The `DetectChangeUsingChangeAnalysisRaster` task generates a raster containing pixel change information using the output change analysis raster from the AnalyzeChangesUsingCCDC task or the AnalyzeChangesUsingLandTrendr task.
Find Argument Statistics	The `FindArgumentStatistics` task is used to extract the dimension value or band index at which a given statistic is attained for each pixel in a multidimensional or multiband raster.
Generate Multidimensional Anomaly	The `GenerateMultidimensionalAnomaly` task is used to compute the anomaly for each slice in a multidimensional raster to generate a multidimensional raster. An anomaly is the deviation of an observation from its standard or mean value.
Generate Trend Raster	The `GenerateTrendRaster` task allows you estimate the trend for each pixel along a dimension for one or more variables in a multidimensional raster.
Manage Multidimensional Raster	The `ManageMultidimensionalRaster` task edits a multidimensional raster by adding or deleting variables or dimensions.
Merge Multidimensional Rasters	The `MergeMultidimensionalRasters` task combines multiple multidimensional raster datasets spatially or across variables and dimensions.
Predict Using Regression Model	Predicts data values using the output from the `TrainRandomTreesRegressionModel` tool.
Predict Using Trend Raster	The `PredictUsingTrendRaster` task is used to compute a forecasted multidimensional raster using the output trend raster from the Generate Trend Raster tool.
Subset Multidimensional Raster	The `SubsetMultidimensionalRaster` task creates a subset of a multidimensional raster by slicing data along defined variables and dimensions.

Tasks that overlays data

The task that overlays data is described in the following table:

Task	Description
LocateRegions	The `LocateRegions` task identifies the best regions, or groups of contiguous cells, from an input utility (suitability) raster that satisfy a specified evaluation criterion and that meet identified shape, size, number, and interregion distance constraints.

Tasks that summarize data

The tasks that summarize data are described in the following table:

Task	Description
Tabulate Area	The `TabulateArea` task calculates cross-tabulated areas between two datasets and outputs a table.
Sample	The `Sample` task creates a table of cell values from a raster, or set of rasters, for defined locations. The locations are defined by raster cells, polygon features, polyline features, or by a set of points.
Summarize Categorical Raster	Generates a table containing the pixel count for each class, in each slice of an input categorical raster.
Summarize Raster Within	The `SummarizeRasterWithin` task summarizes the cells of a raster within the boundaries of zones defined by another dataset.
Zonal Geometry As Table	The `ZonalGeometryAsTable` task calculates the geometry measures for each zone in a dataset and reports the results as a table.
Zonal Statistics	The `ZonalStatistics` task summarizes the cells of a raster within the boundaries of zones defined by another dataset.
Zonal Statistics As Table	The `ZonalStatisticsAsTable` task summarizes the cells of a raster within the boundaries of zones defined by another dataset.

Tasks that use proximity for performing analysis

The tasks that use proximity for performing analysis are described in the following table:

Task	Description
Distance Accumulation	The `DistanceAccumulation` task calculates accumulated distance for each cell to sources, allowing for straight-line distance, cost distance, and true surface distance, as well as vertical and horizontal cost factors.
Distance Allocation	The `DistanceAllocation` task calculates distance allocation for each cell to the provided sources based on straight-line distance, cost distance, and true surface distance, as well as vertical and horizontal cost factors.
Optimal Path As Line	The `OptimalPathAsLine` task calculates the optimal path from a source to a destination as a feature.
Optimal Path As Raster	The `OptimalPathAsRaster` task calculates the optimal path from a source to a destination as a raster.
Optimal Region Connections	The `OptimalRegionConnections` task calculates the optimal connectivity network between two or more input regions.

Legacy tasks that use proximity for performing analysis

Legacy tasks that use proximity for performing analysis are described in the following table:

Task	Description
Calculate Distance	The `CalculateDistance` task calculates the Euclidean distance, direction, and allocation from a single source or set of sources.
Calculate Travel Cost	The `CalculateTravelCost` task calculates the cost distance from a single source or set of sources, while accounting for surface distance and horizontal and vertical cost factors.
Cost Path As Polyline	The `CostPathAsPolyline` task calculates the least-cost path from a source to a destination.
Determine Optimum Travel Cost Network	The `DetermineOptimumTravelCostNetwork` task calculates the optimum cost network from a set of input regions.
Determine Travel Cost Path as Polyline	The `DetermineTravelCostPathAsPolyline` task calculates the least-cost path between sources and destinations.
Determine Travel Cost Paths to Destinations	The `DetermineTravelCostPathsToDestinations` task calculates specific paths between known sources and known destinations.