Deep Learning for Ground and Non-ground Surface Separation: A Feature-based Semantic Segmentation Algorithm for Point Cloud Classification

Precise ground surface topography is crucial for 3D city analysis, digital terrain modeling, natural disaster monitoring, high-density map generation, and autonomous navigation to name a few. Deep learning (DL; LeCun, et al., 2015), a division of machine learning (ML), has been achieving unparalleled success in image processing, and recently demonstrated a huge potential for point cloud analysis. This article presents a feature-based DL algorithm that classifies ground and non-ground points in aerial laser scanning point clouds.

Recent advancements of remote sensing technologies make it possible digitizing the real world in a near automated fashion. LiDAR (Light Detection and Ranging) based point clouds that are a type of remotely sensed georeferenced data, providing detailed 3D information on objects and environment have been recognized as one of the most powerful means of digitization. Unlike imagery, point clouds are unstructured, sparse and of irregular data format which creates many challenges, but also provides huge opportunities for capturing geometric details of scanned surfaces with millimeter accuracy. Classifying and separating non-ground points from ground points largely reduce data volumes for consecutive analyses of either ground or non-ground surfaces, which consequently saves cost and labor, and simplifies further analysis.