Summary
In a raster analysis deployment, this tool runs a trained deep learning model on an input image to produce a classified raster published as a hosted imagery layer in your portal.
Illustration
Usage
The raster analysis (RA) server Python environment must be configured with the proper deep learning framework Python API such as Tensorflow, CNTK, or something similar.
With this tool running, your RA server calls a third-party deep learning Python API (such as TensorFlow or CNTK) and uses the specified Python raster function to process each raster tile.
The input model of this tool will only take a deep learning package (.dlpk) item from the portal.
After the input model is selected or specified, the tool will obtain the model arguments information from the RA server. The tool may fail to obtain such information if your input model is invalid or your RA server isn’t properly configured with the deep learning framework.
Syntax
ClassifyPixelsUsingDeepLearning(inputRaster, inputModel, outputName, {modelArguments}, {processingMode})| Parameter | Explanation | Data Type |
inputRaster | The input image to classify. It can be an image service URL, a raster layer, an image service, a map server layer, or an Internet tiled layer. | Raster Layer; Image Service; MapServer; Map Server Layer; Internet Tiled Layer; String |
inputModel | The input is a URL of a deep learning package (.dlpk) item. It contains the path to the deep learning binary model file, the path to the Python raster function to be used, and other parameters such as preferred tile size or padding. | File |
outputName | The name of the image service of the classified pixels. | String |
modelArguments [modelArguments,...] (Optional) | The function arguments are defined in the Python raster function class referenced by the input model. This is where you list additional deep learning parameters and arguments for experiments and refinement, such as a confidence threshold for adjusting the sensitivity. The names of the arguments are populated by the tool from reading the Python module on the RA server. | Value Table |
processingMode (Optional) | Specifies how all raster items in a mosaic dataset or an image service will be processed. This parameter is applied when the input raster is a mosaic dataset or an image service.
| String |
Derived Output
| Name | Explanation | Data Type |
| outRaster | The output raster dataset. | Raster Layer |
Code sample
This example classifies a raster based on a custom pixel classification using deep learning in a raster analysis deployment and publishes the raster as a hosted imagery layer in your portal.
import arcpy
arcpy.ClassifyPixelsUsingDeepLearning_ra(
"https://myserver/rest/services/landclassification/ImageServer",
"https://myportal/sharing/rest/content/items/itemId",
"classifiedLand", "padding 0")This example classifies a raster based on a custom pixel classification using deep learning in a raster analysis deployment and publishes the raster as a hosted imagery layer in your portal.
#---------------------------------------------------------------------------
# Name: ClassifyPixelsUsingDeepLearning_example02.py
# Requirements: ArcGIS Image Server
# Import system modules
import arcpy
# Set local variables
inImage = "https://myserver/rest/services/ landclassification/ImageServer"
inModel = "https://myportal/sharing/rest/content/items/itemId"
outName = "classifiedLand"
modelArgs = "padding 0"
# Execute Classified Pixels Using raster analysis tool
arcpy.ClassifyPixelsUsingDeepLearning_ra(inImage, inModel, outName, modelArgs)Environments
Licensing information
- Basic: Requires ArcGIS Image Server
- Standard: Requires ArcGIS Image Server
- Advanced: Requires ArcGIS Image Server