The Deep Learning geoprocessing functions allow you to train a deep learning model, detect specific features in an image, classify pixels in a raster dataset.
Deep learning is a type of machine learning artificial intelligence that detects features in imagery using multiple layers in neural networks in which each layer is capable of extracting one or more unique features in the image. The geoprocessing functions in the Deep Learning category take advantage of GPU processing to perform analysis in a timely manner.
These ArcPy geoprocessing functions consume the models that have been trained to detect specific features in third-party deep learning frameworks—such as TensorFlow, CNTK, and PyTorch—and output features or class maps.
| Geoprocessing function | Description |
|---|---|
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. | |
Runs a trained deep learning model on an input raster to produce a classified raster, with each valid pixel having an assigned class 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. | |
Calculates the accuracy of a deep learning model by comparing the detected objects from the Detect Objects Using Deep Learning tool to ground truth data. | |
Runs a trained deep learning model on an input raster to produce a feature class containing the objects it finds. The features can be bounding boxes or polygons around the objects found or points at the centers of the objects. 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. | |
Converts labeled vector or raster data to deep learning training datasets using a remote sensing image. The output is a folder of image chips and a folder of metadata files in the specified format. | |
Identifies duplicate features from the output of the Detect Objects Using Deep Learning tool as a postprocessing step and creates a new output with no duplicate features. The Detect Objects Using Deep Learning tool can return more than one bounding box or polygon for the same object, especially as a tiling side effect. If two features overlap more than a given maximum ratio, the feature with the lower confidence value will be removed. | |
Trains a deep learning model using the output from the Export Training Data For Deep Learning tool. |