Segmentation of Building Roofs from Airborne LiDAR Point Clouds Using Voxel-based Region Growing

Accurate segmentation of building roofs is important for 3D building model reconstruction. However, traditional segmentation methods have some problems such as under-segmentation, over-segmentation, and difficulty in accurately segmenting small surfaces due to the diverse and complex characteristics of building roofs such as varying sizes and shapes, as well as the properties of LiDAR point clouds such as uneven density and large amount of data. To address these problems, this paper proposes a building roof segmentation method using voxel-based region growing to improve the segmentation accuracy from airborne LiDAR point clouds. First, the voxel size is determined based on the density of points derived from the point clouds projected onto the xyplane, and the voxelization of the point cloud is performed. Then, the normal vector and curvature of each voxel are estimated using the PCA method. This leads to the initial roof segmentation results based on voxel growing. During the segmentation process, the voxel with the minimum curvature value is selected as the initial seed voxel, and the surrounding 26-neighborhood voxels are assigned as the growing voxels. The growth is constrained by the angle of the normal vector between the seed voxel and the growing voxels. The new seed voxels to be grown are determined iteratively based on the absolute difference of the curvature values between the current seed voxel and the growing voxels. The growth continues until no new seed voxels appear. This process is repeated by selecting a new initial seed voxel until the segmentation of all voxels is completed. Finally, the final roof surface is obtained through optimization processes such as merging the over-segmented roof surface into the initial segmentation results, repairing the integrity of the roof surface, and extracting small surfaces for complicated buildings. In this paper, airborne LiDAR point cloud data from two regions, i.e., Vaihingen and Toronto, provided by ISPRS official website, are selected to perform roof segmentation experiments of single buildings and building areas. The results show that the completeness, accuracy, and quality of the point cloud segmentation of complicated building roofs are 95.36%~99.58%, 94.83%~100%, and 90.65%~98.28%, respectively. The proposed method can effectively improve the accuracy of roof segmentation based on LiDAR point clouds without under-segmentation and over-segmentation problems, which provides reliable basic data for the automatic construction of 3D building models based on LiDAR data. © 2023 Journal of Geo-Information Science. All rights reserved.