Trees have a complex geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex semantics and can produce superior results. Classifying trees from point cloud data is useful in applications such as high-quality 3D basemap creation, urban planning, and forestry workflows.
License requirements
To complete this workflow, the following are the license requirements:
- ArcGIS Desktop—ArcGIS 3D Analyst extension for ArcGIS Pro
Model details
This model has the following characteristics:
- Input—Point cloud in LAS dataset file format (.lasd) or as per the Classify Point Cloud Using Trained Model tool.
- Output—Classified point cloud with two classes: tree/high-vegetation and background.
- Compute—This workflow is compute intensive and a GPU with compute capability of 6.0 or higher is recommended.
- Architecture—This model uses the PointCNN model architecture implemented in ArcGIS API for Python.
- Extra attributes—Input point clouds should have at least X, Y, Z, and Number of Returns.
- Accuracy metrics—For the validation dataset, the F1-score is 0.970628.
- Class mapping—Points predicted as tree/high-vegetation are mapped to class code 5, and those predicted as background are mapped to class code 0.
Access and download the model
Download the Tree Point Classification pretrained model from ArcGIS Living Atlas of the World. Alternatively, access the model directly from ArcGIS Pro.
- Browse to ArcGIS Living Atlas of the World.
- Sign in with your ArcGIS Online credentials.
- Search for Tree Point Classification and open the item page from the search results.
- Click the Download button to download the model.
You can use the downloaded .dlpk file directly in ArcGIS Pro.
Release notes
The following are the release notes:
| Date | Description |
|---|---|
| October 2020 |
|