GitHub repository
Automatically classifies and labels urban point clouds using data fusion with public datasets and region growing techniques.
What is GitHub repository?
Urban PointCloud Processing is a Python-based toolkit for automatically classifying and labeling urban point clouds, such as those from LiDAR scans. It solves the problem of scarce labeled 3D training data by fusing point clouds with public geospatial datasets (e.g., elevation and topography registries) to generate initial labels for objects like buildings, trees, and street furniture.
Geospatial data scientists, urban planners, and researchers working with 3D point cloud data in urban environments, particularly those focused on automating asset detection and classification without extensive manual labeling.
Developers choose this project because it provides a practical, data-fusion-driven alternative to machine learning methods that require large labeled datasets, offering out-of-the-box tools for urban point cloud labeling with public data integration and region-growing post-processing.
Overview
Repository for automatic classification and labeling of Urban PointClouds using data fusion and region growing techniques.
Use Cases
Best For
- Automatically labeling street furniture (e.g., traffic signs, benches) in urban point clouds
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
