An improved minimum bounding rectangle algorithm for regularized building boundary extraction from aerial LiDAR point clouds with partial occlusions

Abstract

ABSTRACT Airborne Light Detection And Ranging (LiDAR) point cloud is an important data source for building 3D digital cities and can be used to acquire and update urban buildings, roads and etc. However, it is difficult to extract complete and accurate building boundaries from airborne LiDAR point clouds due to partial occlusion, which is mainly caused by adjacent tall trees, particularly in spring and summer. In this paper, we propose an improved minimum bounding rectangle (IMBR) algorithm to extract complete and accurate regularized building boundaries with and without partial occlusion from aerial LiDAR point clouds. The new algorithm only uses LiDAR point cloud and doesn’t need any additional data source. In addition, the algorithm can be applied to buildings with complex shapes. To test the proposed algorithm and compare it with the recursive minimum bounding rectangle (RMBR) algorithm, three datasets with different types of partial occlusions and different shapes were tested. The experimental results show that IMBR can successfully extract the complete and accurate regularized building boundary with or without partial occlusion, and its accuracy is equal to that of RMBR algorithm.