- Related Resources:Add Image, Calculate Density, Calculate Distance, Calculate Travel Cost,Classify, Classify Pixels Using Deep Learning, Convert Feature to Raster, Convert Raster to Feature, Copy Raster, Create Image Collection, Create Viewshed, Delete Image, Delete Image Collection, Detect Objects Using Deep Learning, Determine Optimum Travel Cost Network, Determine Travel Cost Paths to Destinations, Determine Travel Cost Path as Polyline, Export Training Data for Deep Learning, Fill, Flow Accumulation, Flow Direction, Flow Distance, Generate Raster, Install Deep Learning Model, Interpolate Points, List Deep Learning Model Info, Nibble, Query Deep Learning Model Info, Segment, Stream Link, Summarize Raster Within, Uninstall Deep Learning Model, Watershed
- Version Introduced:10.6.1
The Train Classifier task is a service to train image classifiers in a deep learning model and return an .ecs file in JSON. The .ecs file is used in the Classify task.
This task can be used to train a classifier using a single multiband raster, or using a change analysis raster generated by the AnalyzeChangesUsingCCDC or AnalyzeChangesUsingLandTrendr tools. When training using a change analysis raster, the output from the Classify task will be a time series of classified rasters.
The complete JSON rft (uri or by value), the complete XML rft (uri or value), portal item ID, image service URL, cloud raster dataset, or shared raster dataset that will be used for training. If multiple input types are provided, the itemId takes priority.
Syntax: A string describes the input raster
Input raster Example:
Raster dataset example
This is the JSON representation of the training samples. Its format is the same as the JSON converted from a training shapefile that was saved out by the ArcMap image classification toolbar.
Syntax: JSON object describes the training samples.
The classifier algorithm and parameters used in the supervised training.
Random trees example
Support vector machine example
Maximum likelihood example
The complete JSON rft (uri or by value), the complete XML rft (uri or by value), portal item ID, image service URL, cloud raster dataset, or shared raster dataset that will be used for training as the segmented raster input. If multiple inputs are given, itemId takes priority.
Syntax: A string describes the input raster.
Raster dataset example
The string of segment attributes used in the training (separated by semicolon). It is the permutation of the following attributes: COLOR; MEAN; STD; COUNT; COMPACTNESS; RECTANGULARITY.
The field from the inputTrainingSampleJSON object that contains dimension value information. This parameter is required when using the output from the AnalyzeChangesUsingCCDC or AnalyzeChangesUsingLandTrendr tools for classification.
When you submit a request, the task assigns a unique job ID for the transaction.
"jobId": "<unique job identifier>",
"jobStatus": "<job status>"
After the initial request is submitted, you can use the jobId to periodically check the status of the job and messages as described in Checking job status. Once the job has successfully completed, you use the jobId to retrieve the results. To track the status, you can make a request of the following form:
https://<raster analysis tools url>/TrainClassifier/jobs/<jobId>
When the status of the job request is esriJobSucceeded, you can access the results of the analysis by making a request of the following form:
https://<raster analysis tools url>/TrainClassifier/jobs/<jobId>/results/output_Classifier_Definition
The ecd is the value property of the resulting JSON.
The following is a sample request URL for TrainClassifier: