Classify Objects Using Deep Learning (Image Analyst)—ArcGIS Pro

Available with Image Analyst license.

Summary

Runs a trained deep learning model on an input raster and an optional feature class to produce a feature class or table in which each input object or feature has an assigned class or category label.

This tool requires a model definition file containing trained model information. The model can be trained using the Train Deep Learning Model tool or by a third-party training software such as TensorFlow, PyTorch, or Keras. The model definition file can be an Esri model definition JSON file (.emd) or a deep learning model package, and it must contain the path to the Python raster function to be called to process each object and the path to the trained binary deep learning model file.

Usage

You need to install the proper deep learning framework Python API (PyTorch or Keras) in the ArcGIS Pro Python environment; otherwise, an error will occur when you add the Esri model definition file to the tool. The appropriate framework information should be provided by the person who created the Esri model definition file.
To set up your machine to use deep learning frameworks in ArcGIS Pro, see Install deep learning frameworks for ArcGIS.
This tool calls a third-party deep learning Python API (such as PyTorch or Keras) and uses the specified Python raster function to process each object.
You can find sample use cases for this tool on the Esri Python raster function GitHub page. You can also write custom Python modules by following examples and instructions.
The Esri model definition parameter value can be an Esri model definition JSON file (.emd), a JSON string, or a deep learning model package (.dlpk). A JSON string is useful when this tool is used on the server so you can paste the JSON string rather than upload the .emd file. The .dlpk file must be stored locally.

See the sample below for the .emd file.

{
    "Framework": "Keras",
    "ModelConfiguration":"KerasClassifier",
    "ModelFile":"C:\\DeepLearning\\Damage_Classification_Model_V7.h5",
    "ModelType":"ObjectClassification",
    "ImageHeight":256,
    "ImageWidth":256,
    "ExtractBands":[0,1,2],
    "CropSizeFixed": 1,
    "BlackenAroundFeature": 1,
    "ImageSpaceUsed": "MAP_SPACE", 
    "Classes": [
    {
       "Value": 0,
       "Name": "Damaged",
       "Color": [255, 0, 0]
    },
    {
       "Value": 1,
       "Name": "Undamaged",
       "Color": [76, 230, 0]
    }
    ]
}

The CropSizeFixed property defines the crop mode of the raster tile around each object. A value of 1 means a fixed raster tile is used, defined by the ImageHeight and ImageWidth properties in the .emd file. The object is centered within the fixed tile size. A value of 0 means a variable tile size is used, in which the raster tile is cropped using the smallest bounding box around the object.
The BlackenAroundFeature property specifies whether the pixels that are outside each object will be masked. A value of 0 means the pixels outside of the object will not be masked. A value of 1 means the pixels outside of the object will be masked.
The tool can process input imagery that is in map space or in pixel space. Imagery in map space is in a map-based coordinate system. Imagery in pixel space is in raw image space with no rotation and no distortion. The reference system can be specified when generating the training data in the Export Training Data For Deep Learning tool using the Reference System parameter. If the model is trained in a third-party training software, the reference system must be specified in the .emd file using the ImageSpaceUsed parameter, which can be set to MAP_SPACE or PIXEL_SPACE.
The input raster can be a single raster, multiple rasters, or a feature class with images attached. For more information about attachments, see Add or remove file attachments.
For information about requirements for running this tool and issues you may encounter, see Deep Learning frequently asked questions.
For more information about deep learning, see Deep learning in ArcGIS Pro.

Parameters

Label	Explanation	Data Type
Input Raster	The input image used to detect objects. The input can be a single raster or multiple rasters in a mosaic dataset, image service, or folder of images. A feature class with image attachments is also supported.	Raster Dataset; Raster Layer; Mosaic Layer; Image Service; Map Server; Map Server Layer; Internet Tiled Layer; Folder; Feature Layer; Feature Class
Output Classified Objects Feature Class	The output feature class that will contain geometries surrounding the objects or feature from the input feature class, as well as a field to store the categorization label.	Feature Class
Model Definition	The Esri model definition parameter value can be an Esri model definition JSON file (.emd), a JSON string, or a deep learning model package (.dlpk). A JSON string is useful when this tool is used on the server so you can paste the JSON string rather than upload the .emd file. The .dlpk file must be stored locally. 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; String
Input Features (Optional)	The point, line, or polygon input feature class that identifies the location of each object or feature to be classified and labelled. Each row in the input feature class represents a single object or feature. If no input feature class is specified, the tool assumes that each input image contains a single object to be classified. If the input image or images use a spatial reference, the output from the tool is a feature class, in which the extent of each image is used as the bounding geometry for each labelled feature class. If the input image or images are not spatially referenced, the output from the tool is a table containing the image ID values and the class labels for each image.	Feature Class; Feature Layer
Class Label Field (Optional)	The name of the field that will contain the class or category label in the output feature class. If no field name is specified, a new field called ClassLabel will be generated in the output feature class.	String
Processing Mode (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. Process as mosaicked image —All raster items in the mosaic dataset or image service will be mosaicked together and processed. This is the default. Process all raster items separately —All raster items in the mosaic dataset or image service will be processed as separate images.	String
Model Arguments (Optional)	The function arguments defined in the Python raster function class. This is where additional deep learning parameters and arguments for experiments and refinement are listed, such as a confidence threshold for adjusting the sensitivity. The names of the arguments are populated from the Python module.	Value Table

ClassifyObjectsUsingDeepLearning(in_raster, out_feature_class, in_model_definition, {in_features}, {class_label_field}, {processing_mode}, {model_arguments})

Name	Explanation	Data Type
in_raster	The input image used to detect objects. The input can be a single raster or multiple rasters in a mosaic dataset, image service, or folder of images. A feature class with image attachments is also supported.	Raster Dataset; Raster Layer; Mosaic Layer; Image Service; Map Server; Map Server Layer; Internet Tiled Layer; Folder; Feature Layer; Feature Class
out_feature_class	The output feature class that will contain geometries surrounding the objects or feature from the input feature class, as well as a field to store the categorization label.	Feature Class
in_model_definition	The in_model_definition parameter value can be an Esri model definition JSON file (.emd), a JSON string, or a deep learning model package (.dlpk). A JSON string is useful when this tool is used on the server so you can paste the JSON string rather than upload the .emd file. The .dlpk file must be stored locally. 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; String
in_features (Optional)	The point, line, or polygon input feature class that identifies the location of each object or feature to be classified and labelled. Each row in the input feature class represents a single object or feature. If no input feature class is specified, the tool assumes that each input image contains a single object to be classified. If the input image or images use a spatial reference, the output from the tool is a feature class, in which the extent of each image is used as the bounding geometry for each labelled feature class. If the input image or images are not spatially referenced, the output from the tool is a table containing the image ID values and the class labels for each image.	Feature Class; Feature Layer
class_label_field (Optional)	The name of the field that will contain the class or category label in the output feature class. If no field name is specified, a new field called ClassLabel will be generated in the output feature class.	String
processing_mode (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. PROCESS_AS_MOSAICKED_IMAGE —All raster items in the mosaic dataset or image service will be mosaicked together and processed. This is the default. PROCESS_ITEMS_SEPARATELY —All raster items in the mosaic dataset or image service will be processed as separate images.	String
model_arguments [model_arguments,...] (Optional)	The function arguments defined in the Python raster function class. This is where additional deep learning parameters and arguments for experiments and refinement are listed, such as a confidence threshold for adjusting the sensitivity. The names of the arguments are populated from the Python module.	Value Table

Code sample

ClassifyObjectsUsingDeepLearning example 1 (Python window)

This example generates a new feature class with a label field to classify damaged and undamaged buildings in an image.

# Import system modules  
import arcpy  
from arcpy.ia import *  
 
# Check out the ArcGIS Image Analyst extension license 
arcpy.CheckOutExtension("ImageAnalyst") 
 
# Execute 
ClassifyObjectsUsingDeepLearning("C:/Data/Input_Image.tif", 
	"C:/Data/MyProject.gdb/Classified_Results", "C:/Data/BuildingDamageClassifier.emd", 
	"C:/Data/building_footprints.shp","Class_Label", 
	"PROCESS_AS_MOSAICKED_IMAGE", "padding 0;batch_size 4")

ClassifyObjectsUsingDeepLearning example 2 (stand-alone script)

This example generates a new feature class with a label field to classify damaged and undamaged buildings in an image.

# Import system modules  
import arcpy  
from arcpy.ia import *  
 
# Check out the ArcGIS Image Analyst extension license 
arcpy.CheckOutExtension("ImageAnalyst") 
 
# Define input parameters
in_raster = "C:/Classify_Objects/input_image.tif" 
out_feature_class = "C:/Classify_Objects/MyProject.gdb/classified_results" 
in_model_definition = "C:/Classify_Objects/BuildingDanmageClassifier.emd" 
in_features = "C:/Classify_Objects/building_footprints.shp" 
class_label_field = "Damaged_or_Undamaged"
model_arguments = "padding 0;batch_size 4"
process_all_raster_items = "PROCESS_AS_MOSAICKED_IMAGE"

# Execute 
ClassifyObjectsUsingDeepLearning(in_raster, out_feature_class, in_model_definition,
	in_features, class_label_field,  
	process_all_raster_items, model_arguments)

Environments

Output Coordinate System, Geographic Transformations, Extent, Cell Size, Processor Type, GPU ID, Parallel Processing Factor

Licensing information

Basic: Requires Image Analyst
Standard: Requires Image Analyst
Advanced: Requires Image Analyst