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

Abstract

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.