Types of raster operations

ArcGIS provides a comprehensive suite of raster analysis capabilities you can use in your applications. The operations can be accessed using interactive and .

The operations are categorized into groups based on the functionality they provide:

  • Summarize data: Summarize raster values within zones or areas.
  • Analyze patterns: Identify, quantify, and visualize spatial patterns in raster data.
  • Proximity analysis: Analyze distance relationships and spatial patterns within raster data.
  • Terrain analysis: Enable a wide range of applications like site selection, hydrologic modeling, visibility analysis, solar radiation mapping, and more by deriving key terrain parameters and characteristics from elevation data.
  • Manage data: Prepare raster inputs for further analysis and enable workflows like creating seamless raster mosaics, clipping rasters to areas of interest, reclassifying values, and converting between raster and vector data formats.
  • Deep learning: Tasks for object detection, pixel/image classification, training data preparation, and model training - enabling end-to-end deep learning workflows for raster analysis.
  • Generalize: Simplify input raster datasets by reducing the number of unique cell values, effectively reducing data volume and complexity
  • Multidimensional analysis: Process, analyze, manage, and extract information from complex multidimensional raster datasets composed of multiple variables and dimensions.
  • Classify data: Prepare segmented or pixel-based rasters for classification.
  • Overlay: Combine two or more raster datasets into a single output raster through weighted, arithmetic, max/min, and sum overlay methods.

Summarize data

Map Viewer tool nameREST API OperationDescriptionResultExample
Zonal StatisticsSummarize Raster WithinSummarize the cells of a raster within the boundaries of zones defined by another dataset.A new Summarize raster within
Zonal Statistics As TableZonal Statistics As TableSummarize the cells of a raster within the boundaries of zones defined by another dataset.A new . Zonal statistics as table
Not availableSummarize Categorical RasterGenerates a table containing the pixel count for each class, in each slice of an input categorical raster.A new . Summarize Categorical Raster
Not availableSampleCreate a table of cell values from a raster, or set of rasters, for defined locations.A new . Sample

Analyze patterns

Map Viewer tool nameREST API OperationDescriptionResultExample
Calculate DensityCalculate DensityCreates a density layer from point features by spreading known quantities of some phenomenon (represented as attributes of the points) across the raster.
Calculate density image
Interpolate PointsInterpolate PointsPredict values at new locations based on measurements from a collection of points. Interpolate points image
Not availableCompute Change RasterEvaluate the difference between two input rasters. Compute change raster image

Proximity analysis

Map Viewer tool nameREST API OperationDescriptionResultExample
Distance AccumulationDistance AccumulationCalculates the accumulated distance for each cell to sources, allowing for straight-line distance, cost distance, true surface distance, as well as vertical and horizontal cost factors. Distance accumulation image
Distance AllocationDistance AllocationCalculates the distance allocation for each cell to the provided sources based on straight-line distance, cost distance, true surface distance, as well as vertical and horizontal cost factors.

Distance allocation image
Optimal Path As LineOptimal Path As LineCalculates the optimal path from a source to a destination as a feature. Path as line image
Optimal Path As RasterOptimal Path As RasterCalculates the optimal path from a source to a destination as a raster.
Path as raster image
Optimal Region ConnectionsOptimal Region ConnectionsCalculates the optimal connectivity network between two or more input regions. Region connections image

Terrain analysis

Map Viewer tool nameOperationDescriptionResultExample
Geodesic ViewshedCreateViewshedUse 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. CreateViewshedImage
Surface ParametersSurfaceParametersDetermine the parameters of a surface raster such as types of curvatures, slope, and aspect. SurfaceParametersImage
Derive Continuous FlowDeriveContinuousFlowGenerates a raster of accumulated flow into each cell from an input surface raster with no prior sink or depression filling required.
FillFillFills the sinks in a surface raster to remove small imperfections in the data. Fill Image
Flow AccumulationFlow accumulationCreate a raster of accumulated flow into each cell. A weight factor can optionally be applied. Fill Image
Flow DirectionFlowDirectionCreate a raster of flow direction from each cell to its steepest downslope neighbor. Fill Image
Flow DistanceFlowDistanceComputes the downslope horizontal or vertical distance to cells in a stream or river into which they flow. Fill Image
Stream LinkStreamLinkAssigns unique values to sections of a raster linear network between intersections. Fill Image
WatershedWatershedDetermines the contributing area above a set of cells in a raster. Fill Image

Manage Data

Map Viewer tool nameREST API OperationDescriptionResultExample
Not availableAdd ImageAdd new images to an existing image collection. AddImageImage
Convert Feature to RasterConvert Feature to RasterConverts point, line, or polygon feature dataset to a raster. ConvertFeatureToRasterImage
Not availableConvert Raster Function TemplateConverts a raster function template between JSON and XML formats.
Convert Raster to FeatureConvert Raster to FeatureConverts a raster to a point, line, or polygon feature dataset. ConvertRasterToFeatureImage
Not availableCopy RasterTakes a 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. CopyRasterImage
Not availableCreate Image CollectionTakes multiple image items as input, creates an 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. CreateImageCollectionImage
Not availableDelete ImageRemove existing images from an image collection. The service will only delete the raster item in the mosaic dataset and will not remove the source image. DeleteImageImage
Not availableDelete Image CollectionDeletes 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. DeleteImageCollectionImage
Not availableDownload RasterDownload an image or partial image at a designated resolution. The input image service must be configured to allow pixel data download. DownloadRasterImage
Not availableGenerate RasterExecute 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. GenerateRasterImage

Deep Learning

Map Viewer tool nameREST API OperationDescriptionResultExample
Classify Objects Using Deep LearningClassifyObjectsUsingDeepLearningClassify 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. ClassifyObjectsDL
Classify Pixels Using Deep LearningClassify Pixels Using Deep LearningClassify pixels using the designated deep learning model and generate an image service for the classified raster. ClassifyPixelsUsingDeepLearning
Detect Change Using Deep LearningDetectChangeUsingDeepLearningDetect change between two rasters.
Detect Objects Using Deep LearningDetectObjectsUsingDeepLearningDetect objects from imagery data using the designated deep learning model and generate a feature service for the detected objects. DetectObjects
Not availableExportTrainingDataForDeepLearningGenerate training sample image chips from the input imagery data with labeled vector data or classified images.Data store path string where the output image chips, labels, and metadata files will be stored. ExportTrainingData
Not availableInstallDeepLearningModelInstall 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.Path string to uploaded package (.emd) InstallDeepLearningModel
Not availableListDeepLearningModelsList all the installed deep learning models on the raster analysis image server.JSON Array of installed models ListDeepLearningModels
Not availablePublishDeepLearningModelPublishes 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. of published model PublishDeepLearningModel
Not availableQueryDeepLearningModelInfoExtract the deep learning model specific settings from the model package item or model definition file.JSON structure describing the deep learning model. QueryDeepLearningModel
Not availableTrainDeepLearningModelTrain a deep learning model using the output from the ExportTrainingDataforDeepLearning operation. This task can also be used to write the deep learning model package to a file share data store location.Portal (.dlpk) TrainDeepLearningModel
Not availableUninstallDeepLearningModelUninstall 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. of uninstalled model UninstallDeepLearningModel

Genaralize

Map Viewer tool nameREST API OperationDescriptionResultExample
NibbleNibbleReplaces the input cells corresponding to a mask with the values of the nearest neighbors. Nibble Image

Multidimensional analysis

Map Viewer tool nameREST API OperationDescription
Aggregate Multidimensional RasterThe 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 CCDCThe 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 LandTrendrThe 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 TransposeThe 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 RasterThe 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 StatisticsThe 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 AnomalyThe 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 RasterThe GenerateTrendRaster task allows you estimate the trend for each pixel along a dimension for one or more variables in a multidimensional raster.
Manage Multidimensional RasterThe ManageMultidimensionalRaster task edits a multidimensional raster by adding or deleting variables or dimensions.
Merge Multidimensional RastersThe MergeMultidimensionalRasters task combines multiple multidimensional raster datasets spatially or across variables and dimensions.
Predict Using Regression ModelPredicts data values using the output from the TrainRandomTreesRegressionModel tool.
Predict Using Trend RasterThe PredictUsingTrendRaster task is used to compute a forecasted multidimensional raster using the output trend raster from the Generate Trend Raster tool.
Subset Multidimensional RasterThe SubsetMultidimensionalRaster task creates a subset of a multidimensional raster by slicing data along defined variables and dimensions.

Classify Data

OperationDescription
ClassifyThe 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 UnmixingThe LinearSpectralUnmixing task performs subpixel classification and calculates the fractional abundance of end members for individual pixels.
SegmentThe Segment task groups adjacent pixels that have similar spectral and spatial characteristics into segments.
Train ClassifierThe 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 ModelModels the relationship between explanatory variables (independent variables) and a target dataset (dependent variable).

Overlay

OperationDescription
LocateRegionsThe 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.

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