ArcGIS Pro allows you to use statistical or machine learning classification methods to classify point clouds. Deep learning is a type of machine learning that relies on multiple layers of nonlinear processing for feature identification and pattern recognition described in a model. You can integrate deep learning models with ArcGIS Pro for point classification.
The workflow is represented in the diagram below.
| Step | Description |
|---|---|
| The first step to use deep learning with point clouds is to prepare the point cloud data for training. The Prepare Point Cloud Training Data tool generates data for training and validating of a convolutional neural network for point cloud classification. |
| Use the Train Point Cloud Classification Model tool to train a deep learning model for point cloud classification. |
| Use the trained model to run the Classify Point Cloud Using Trained Model tool. |
Get started with deep learning
All deep learning geoprocessing tools in ArcGIS Pro require that the supported deep learning frameworks libraries be installed.
For instructions on how to install deep learning packages, see the Deep Learning Libraries Installer for ArcGIS Pro.
Note:
Each version of ArcGIS Pro requires specific versions of deep learning libraries. When you upgrade ArcGIS Pro, you need to install the deep learning libraries that correspond to that version of ArcGIS Pro. For the list of libraries required at each version, see Deep learning in ArcGIS Pro FAQ
.
Requirements
- Deep learning libraries listed above.
- GPU: NVIDIA GPU with CUDA Compute Capability (CC). Required and recommended versions of CC are listed on the Deep Learning Libraries Installer.
- Minimum dedicated GPU RAM is 8 GB. This is more than minimum requirement for image-based deep learning tools because point cloud processing requires more memory. For additional information on GPU requirements, see Deep learning frequently asked questions.
- ArcGIS 3D Analyst extension license.