Raster analysis allows you to perform analysis of large raster datasets using the ArcGIS Image Server. This allows you to analyze more data faster by harnessing the power of the server. The toolsets currently available through the Portal for ArcGIS web user experience are Summarize Data, Analyze Patterns, Use Prxoimity, Analyze Image, Analyze Terrain, Manage Data, and Deep Learning.
This toolset contains a tool for calculating some statistics for a raster layer within area boundaries that you define.
Summarize Raster Within |
Calculates some summary statistics for raster cells within defined areas.
Some example applications are the following:
These tools help you identify, quantify, and visualize spatial patterns in your data.
Calculate Density |
Density analysis takes known quantities of some phenomenon and creates a density map by spreading these quantities across the map. You can use this tool, for example, to show concentrations of lightning strikes or tornadoes, access to health care facilities, and population densities.
Interpolate Points |
This tool allows you to predict values at new locations based on measurements found in a collection of points. The tool takes point data with values at each point and returns areas classified by predicted values. You can use this tool, for example, to predict rainfall levels across a watershed based on measurements taken at individual rain gauges.
These tools help you answer one of the most common questions posed in spatial analysis: "What is near what?"
Calculate Distance |
Calculates the Euclidean distance, direction and allocation from a single or set of sources. You can use this tool to determine how far a location is to a road, a building or a park. You can also determine which direction you must travel from a location to return back to a source in the most direct way. You can see for every location in your study area which is the closest source.
Determine Optimum Travel Cost Network |
Calculates the optimum cost network from a set of input regions.
Determine Travel Cost Path As Polyline |
Calculates the least cost polyline path between sources and known destinations.
The following tool helps you analyze images.
Apply Raster Function Template |
Processes your imagery with the chain of functions, as specified by the raster function template.
Monitor Vegetation |
Performs an arithmetic operation on the bands of a multiband raster layer to reveal vegetation coverage information of the study area.
These tools help you analyze raster surfaces.
Calculate Slope |
Identifies a surface that shows the slope of the input elevation data. Slope represents the rate of change of elevation for each digital elevation model (DEM) cell.
Derive Aspect |
Identifies the downslope direction of the maximum rate of change in value from each cell to its neighbors. Aspect can be thought of as the slope direction.
Create Viewshed |
Determines the locations on a raster surface that are visible to a set of observers.
Watershed |
Determines the contributing area above a set of cells in a raster.
These tools are used for both the day-to-day management of geographic data and for combining data prior to analysis.
Extract Raster |
Extract cells from a raster based on value, shape, or the extent of a different dataset.
Remap Values |
Change the individual or ranges of cell values to new values.
Convert Feature to Raster |
Create a new raster dataset from an existing feature dataset.
Convert Raster to Feature |
Create a new feature dataset from an existing raster dataset.
These tools are used to detect specific features in an image or to classify pixels in a raster dataset. Deep learning is a type of artificial intelligence machine learning method that detects features in imagery using multiple layers in neural networks where each layer is capable of extracting one or more unique features in the image. These tools consume the models that have been trained to detect specific features in third-party deep learning frameworks—such as TensorFlow, CNTK and Keras—and output features or class maps.
Classify Pixels Using Deep Learning |
Runs a trained deep learning model on an input raster to produce a classified raster, with each valid pixel having a class label assigned.
Detect Objects Using Deep Learning |
Runs a trained deep learning model on an input raster to produce a feature class containing the objects it finds. The features can be bounding boxes or polygons around the objects found, or points at the centers of the objects.