Integrating OpenStreetMap tags for efficient LiDAR point cloud classification using graph neural networks

ABSTRACT

The urban environment exhibits significant vertical variations, Light Detection and Ranging (LiDAR) point cloud classification can provide insights for the 3D morphology of the urban environment. Introducing the adjacency relationships between urban objects can enhance the accuracy of LiDAR point cloud classification. Graph Neural Network (GNN) is a popular architecture to infer the labels of urban objects by utilizing adjacency relationships. However, existing methods ignored the power of the known labels of urban objects, such as crowd-sourced tagged labels from OpenStreetMap (OSM) data, in the inferring process. Therefore, this study proposes a strategy introduces OSM data into GNN for LiDAR point cloud classification. First, we perform an over-segmentation of the LiDAR point cloud to obtain superpoints, which act as basic elements for constructing superpoint adjacency graphs. Second, PointNet is applied to embed superpoint features and edge features are generated using these superpoint features. Finally, OSM data is associated with some part of superpoints and incorporated into the GNN to update the embedded features of superpoints. The results demonstrate that the GNN with OSM data significantly improves the classification accuracy of original GNN. The improvement highlights taking advantage of crowd-sourced geoinformation in LiDAR point cloud classification for understanding 3D urban landscape.

KEYWORDS:

• LiDAR point cloud classification
• OpenStreetMap data
• Graph neural networks
• Multi-modal data integration
• Prior knowledge

Acknowledgements

The authors are grateful to the editors and the anonymous reviewers for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The Vaihingen data set was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF): http://www.ifp.uni-stuttgart.de/dgpf/DKEPAllg.html. The LiDAR point cloud were provided by the International Society for Photogrammetry and Remote Sensing (ISPRS): https://www.isprs.org/education/benchmarks.aspx. OSM was downloaded from https://www.opestreetmap.org/#map=15/48.9288/8.9638&layers=N.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (42071440), the National Key R&D Program of China (2023YFC3006701), the Natural Science Foundation of Jiangsu Province of China (BK20201257), the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources (KF-2021-06-004), the Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR (KFKT-2022-09), and the Fundamental Research Funds for the Central Universities of China (B210201049).