Available with Image Server
The Detect Change Using Deep Learning tool uses a trained deep learning model to detect change between two raster layers.
The output is a hosted imagery layer.
Example
Given two spectrally similar imagery layers and a deep learning model indicating changed areas, detect the areas that have changed between the two imagery layers.
Usage notes
Detect Objects Using Deep Learning includes configurations for input layers, model settings, and the result layer.
Input layers
The Input layers group includes the following parameters:
- The input raster before change is the imagery layer that represents the previous imagery layer. The imagery layer selected should be based on the requirements of the deep learning model that will be used to classify the pixels.
- The input raster after the change is the imagery layer that represents the after imagery layer. The imagery layer selected should be based on the requirements of the deep learning model that will be used to classify the pixels.
Model settings
The Model settings group includes the following parameters:
- Model for change detection is the deep learning model that will be used to detect the change. 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.
Result layer
The Result layer group includes the following parameters:
- Output name specifies the name of the layer that is created and displayed. 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 is Full extent. This default is different from Map Viewer Classic in which Use current map extent is enabled by default.
- Snap raster
- Cell size
- Parallel processing factor
- Processor type
Output
The output is a classified thematic imagery layer based on the classification scheme defined in the deep learning model.
Usage requirements
This tool requires the following user type and configurations:
Resources
Use the following resources to learn more:
- Detect Change Using Deep Learning in ArcGIS REST API
- detect_change_using_deep_learning in ArcGIS API for Python.
- Detect Change Using Deep Learning in ArcGIS Pro.