The Classify Objects Using Deep Learning tool runs a deep learning model on an imagery layer to produce a feature layer or table in which each input object is classified.
The output is a hosted feature layer.
Examples
Example scenarios for the use of this tool include the following:
- Assess damaged buildings after a natural disaster. With a feature layer of building footprints and an imagery layer showing the damaged areas, the tool can indicate whether the existing buildings have been damaged.
- Indicate tree canopy health for existing trees. With a feature layer of tree canopies and an imagery layer showing the current tree canopy, the tool can indicate whether the existing trees are healthy or under stress.
Usage notes
Classify Objects Using Deep Learning includes configurations for input layers, model settings, and the result layer.
Input layer
The Input layer group includes the following parameters:
- Input imagery layer or feature layer is the imagery layer or layers that will be used to classify objects. The imagery layer selected should be based on the requirements of the deep learning model that will be used to classify the objects. The imagery layer can be multidimensional or an image collection.
- Input feature layer is the features indicating the positions to be classified. Each row in the input feature layer represents a single object. If no input feature layer is specified, it is assumed that each input image contains a single object to be classified.
- Processing mode specifies how the raster items in the imagery layer will be processed. The options are as follows:
- 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.
Model settings
The Model settings group includes the following parameters:
- Model for object classification is the deep learning model that will be used to classify the objects. The deep learning model must be located on ArcGIS Online to be selected in the tool. You can select your own model, a publicly available model in ArcGIS Online, or a model from ArcGIS Living Atlas of the World.
- Model arguments specifies the function arguments defined in the Python raster function class. 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.
- Output class label field name is the field name that will contain the classification label in the output hosted feature layer or table.
Result layer
The Result layer group includes the following parameters:
- Output name specifies the name of the layer that is created and added to the map. The name must be unique. If a layer with the same name already exists in your organization, the tool will fail and you will be prompted to use a different name.
- Save in folder specifies the name of a folder in My content where the result will be saved.
Environments
Analysis environment settings are additional parameters that affect a tool's results. You can access the tool's analysis environment settings from the Environment settings parameter group.
This tool honors the following analysis environments:
- Output coordinate system
- Processing extent
Note:
The default processing extent in Map Viewer is Full extent. This default is different from Map Viewer Classic in which Use current map extent is enabled by default.
- Cell size
Credits
This tool consumes credits.
Use Estimate credits to calculate the number of credits that will be required to run the tool. For more information, see Understand credits for spatial analysis.
Outputs
This tool includes the following outputs:
- A hosted feature layer with the objects or features labeled based on the classification determined by the deep learning model
- A table with the locations labeled based on the classification determined by the deep learning model
Usage requirements
This tool requires the following user type and configurations:
- Professional or Professional Plus user type
- Publisher, Facilitator, or Administrator role, or an equivalent custom role with the Imagery Analysis privilege
Resources
Use the following resources to learn more:
- Classify Objects Using Deep Learning in ArcGIS REST API
- classify_objects function in ArcGIS API for Python
- Detect Objects Using Deep Learning in ArcGIS Online
- Classify Pixels Using Deep Learning in ArcGIS Online
- Classify Objects Using Deep Learning in ArcGIS Pro