Automated extraction of building instances from dual-channel airborne LiDAR point clouds

With the rapid development of Light Detection And Ranging (LiDAR) systems, the novel dual-channel airborne LiDAR systems have emerged to provide more complete and precise data than traditional scanners for building instance extraction since 2013. RIEGL VQ-1560i, launched in 2016, is a state-of-the-art dual-channel LiDAR system, which is capable of capturing dense points on building rooftops and façades simultaneously, due to the unique and innovative bidirectional scanning angle. Our proposed method is the first ever to use dual-channel airborne LiDAR data for subsequent point clouds processing. The main challenges of the new LiDAR data are significant amount of points, complex data structure and multi-class targets. We proposed a preprocessing-free building instance extraction method consisting of three steps, i.e., point cloud reorganization, rasterization, and constraint-based labeling for improving the extraction performance. First, point cloud reorganization, consisting of point distribution-based slicing, coarse 3D semantic segmentation, and top-down merging, is used to reorganize point cloud scene into interrelated point groups. This greatly reduces the processing difficulty and computational burden of complex structures while removing multiple classes of non-building points. Second, we rasterize the point groups into images to further reduce computational complexity while improving processing efficiency. Finally, we utilize the upper and lower structural relationship of buildings to label them and then remap into 3D buildings. Experimental results on six test point cloud scenes demonstrate the outstanding performance of the proposed preprocessing-free method. For semantic-level performance, our method achieves 95.36% in average recall and 93.59% in average F1-score. While for instance-level performance, our approach reaches 92.86% and 98.31% in quality on two public test scenes, respectively.

Abstract licence: CC BY-NC-ND

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