Unleashing the power of OpenStreetMap tags: a graph neural network approach for efficient LiDAR point cloud classification

Guoli Li; Xiaoqiang Liu; Xinyu Cai; Yao Chen; Yanming Chen

doi:10.1117/12.3010349

15 November 2023 Unleashing the power of OpenStreetMap tags: a graph neural network approach for efficient LiDAR point cloud classification

Guoli Li, Xiaoqiang Liu, Xinyu Cai, Yao Chen, Yanming Chen

Author Affiliations +

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 128152T (2023) https://doi.org/10.1117/12.3010349
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

When employing Graph Neural Networks (GNN) for point cloud classification, global information is passed through the iteration of hidden states of graph nodes. However, this method is often associated with high memory and time costs. To address this issue, we propose a strategy that integrates prior OpenStreetMap (OSM) tags into LiDAR point cloud classification, which enhance iteration speeds by fixing the initial hidden states of certain graph nodes. First, we perform over-segmentation of the LiDAR point cloud to obtain super points and embedded features. Then, OSM tags are affixed to the super points and incorporated into the GNN to update other super point embedded features. Our results demonstrate that coupling OSM with GNN significantly improves classification accuracy for roof, tree, and impervious surface categories.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guoli Li, Xiaoqiang Liu, Xinyu Cai, Yao Chen, and Yanming Chen "Unleashing the power of OpenStreetMap tags: a graph neural network approach for efficient LiDAR point cloud classification", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 128152T (15 November 2023); https://doi.org/10.1117/12.3010349

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