Scene Adaptive Building Individual Segmentation Based on Large-Scale Airborne LiDAR Point Clouds

Building individual segmentation plays a crucial role in building querying, management, analysis, and attribute addition. Previous research on this topic has primarily concentrated on small-scale scenes and single-type buildings. However, when dealing with complex scenes that contain diverse buildings, existing methods for building individual segmentation often encounter challenges, such as excessive undersegmentation and oversegmentation. To tackle this issue, we propose a scene adaptive building individual segmentation (SABIS) based on large-scale airborne LiDAR point clouds. The method first segments the roof object and then extract elevation feature and area feature of the roof object. Based on these features, the building point cloud is classified into two categories: urban scene buildings and rural residential scene buildings. Finally, for urban scene buildings, the building individual segmentation method based on the cylinder model consistency is used. For rural residential scene buildings, the building individual segmentation method based on bidirectional saliency features is employed. In this article, the proposed SABIS algorithm is quantitatively evaluated by using three large scene datasets at home and abroad and four benchmark methods. All kinds of accuracy are significantly better than the most advanced algorithms.