The classification of point cloud datasets to identify points that belong to a building class is a fundamental GIS use case. Classification of building points is a key step in creating 3D models/digital twins and in change detection workflows for infrastructure projects. The classification of building points is also crucial in applications where buildings' vicinity from objects of interest such as power poles, wires, trees, and so on is needed to be determined.
This model is designed to classify building points, for any geography. Its predictions for skyscrapers are less consistent with changes in geography as compared to stand-alone or connected small- to medium-sized buildings. As the model is targeted for buildings, in the case of very large warehouses, the precision might be reduced in some cases. The model was trained on airborne lidar datasets, without high or low noises and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For these or similar cases, this pretrained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block, and extra attributes should match those of the data originally used for training this model.
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: building and background.
- Compute—This workflow is compute intensive and a GPU with compute capability of 6.0 and 8 GB of dedicated VRAM or higher is recommended.
- Applicable geographies—The model is expected to work within any geography. However, results can vary for datasets that are statistically dissimilar to training data.
- Architecture—This model uses the RandLANet architecture as implemented in ArcGIS API for Python.
- Extra attributes—Input point clouds should have at least x, y, and z.
- Class mapping—Points predicted as building are mapped to class code 6, and background is mapped to class code 0.
Access and download the model
Download the Building 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 Building 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 |
|---|---|
| June 2024 | First release of Building Point Classification |