Extraction of Building Roof Contours from Airborne LiDAR Point Clouds Based on Multidirectional Bands

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 1; p. 190
• Main Authors: Wang, Jingxue, Zang, Dongdong, Yu, Jinzheng, Xie, Xiao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2024
• Subjects: airborne LiDAR point clouds; Algorithms; average point spacing; Banded structure; Banding; building outer contour; building roof contour extraction; Buildings; Comparative analysis; Contours; data collection; Densification; Density distribution; Design and construction; Edge joints; Efficiency; Identification and classification; Lidar; Machine learning; multidirectional bands; Occlusion; Optical properties; Optical radar; Optimization; Parameters; Robustness; Roofs; Three dimensional models; topology; China
• DOI: 10.3390/rs16010190
• ISSN: 2072-4292

Because of the complex structure and different shapes of building contours, the uneven density distribution of airborne LiDAR point clouds, and occlusion, existing building contour extraction algorithms are subject to such problems as poor robustness, difficulty with setting parameters, and low extraction efficiency. To solve these problems, a building contour extraction algorithm based on multidirectional bands was proposed in this study. Firstly, the point clouds were divided into bands with the same width in one direction, the points within each band were vertically projected on the central axis in the band, the two projection points with the farthest distance were determined, and their corresponding original points were regarded as the roof contour points; given that the contour points obtained based on single-direction bands were sparse and discontinuous, different banding directions were selected to repeat the above contour point marking process, and the contour points extracted from the different banding directions were integrated as the initial contour points. Then, the initial contour points were sorted and connected according to the principle of joining the nearest points in the forward direction, and the edges with lengths greater than a given threshold were recognized as long edges, which remained to be further densified. Finally, each long edge was densified by selecting the noninitial contour point closest to the midpoint of the long edge, and the densification process was repeated for the updated long edge. In the end, a building roof contour line with complete details and topological relationships was obtained. In this study, three point cloud datasets of representative building roofs were chosen for experiments. The results show that the proposed algorithm can extract high-quality outer contours from point clouds with various boundary structures, accompanied by strong robustness for point clouds differing in density and density change. Moreover, the proposed algorithm is characterized by easily setting parameters and high efficiency for extracting outer contours. Specific to the experimental data selected for this study, the PoLiS values in the outer contour extraction results were always smaller than 0.2 m, and the RAE values were smaller than 7%. Hence, the proposed algorithm can provide high-precision outer contour information on buildings for applications such as 3D building model reconstruction.