Building Boundary Tracing and Regularization from Airborne Lidar Point Clouds

Building boundary is necessary for the real estate industry, flood management, and homeland security applications. The extraction of building boundary is also a crucial and difficult step towards generating city models. This study presents an approach to the tracing and regularization of building boundary from raw lidar point clouds. The process consists of a sequence of four steps: separate building and non-building lidar points; segment lidar points that belong to the same building; trace building boundary points; and regularize the boundary. For separation, a slope based 1D bi-directional filter is used. The segmentation step is a region-growing approach. By modifying a convex hull formation algorithm, the building boundary points are traced and connected to form an approximate boundary. In the final step, all boundary points are included in a hierarchical least squares solution with perpendicularity constraints to determine a regularized rectilinear boundary. Our tests conclude that the uncertainty of regularized building boundary tends to be linearly proportional to the lidar point spacing. It is shown that the regularization precision is at 18 percent to 21 percent of the lidar point spacing, and the maximum offset of the determined building boundary from the original lidar points is about the same as the lidar point spacing. Limitation of lidar data resolution and errors in previous filtering processes may cause artefacts in the final regularized building boundary. This paper presents the mathematical and algorithmic formulations along with stepwise illustrations. Results from Baltimore city, Toronto city, and Purdue University campus are evaluated. Introduction Airborne lidar (light detection and ranging) technology provides georeferenced 3D dense point measurements over a reflective surface on the ground (Baltsavias, 1999; Wehr and Lohr, 1999). This paper discusses extracting building boundary outlines from raw lidar datasets over urban areas. As a prerequisite for many building extraction approaches, the ground points need to be separated from non-ground points, for which a number of methods have been developed. Representatives include early work by Lindenberger (1993) and Kilian et al. (1996) based on mathematical morphology; by Kraus and Pfeifer (1998) using least squares surface fitting; and by Axelsson (1999) and Vosselman (2000) using slope-based filters. Some recent effort focuses on the performance comparison and evaluation as reported Building Boundary Tracing and Regularization from Airborne Lidar Point Clouds Aparajithan Sampath and Jie Shan in Sithole and Vosselman (2004), Zhang et al. (2004), Shan and Sampath (2005), and Zhang and Whitman (2005), to which the readers may refer for methodological details and a comprehensive review on this topic. Many attempts have been made on building extraction from lidar points or a digital surface model (DSM) generated from stereo images. Weidner and Förstner (1995), Brunn and Weidner (1997), and Ameri (2000) use the difference between DSM and digital terrain model (DTM) to determine the building outlines. Haala et al. (1998), Brenner (2000), and Vosselman and Dijkman (2001) use building plan maps, and Sohn and Dowman (2003) use Ikonos imagery to facilitate the detection and reconstruction of buildings from lidar points. Masaharu and Hasegawa (2000) segment building polygons from neighboring non-building regions, and use boundary-tracing methods to segment individual buildings. Wang and Schenk (2000) generate the triangulated irregular network (TIN) model from the lidar point clouds. Triangles are then grouped based on the orientation and position to form larger planar segments. The intersection of such planar segments results in building corners or edges. Al-Harthy and Bethel (2002) determine the building footprints by subtracting DTM from DSM obtained by initially filtering out the non-ground points. The building polygon outline is then obtained by using a rotating template to determine the angle of highest cross-correlation, which suggests the dominant directions of the building. Morgan and Habib (2002) first determine the breaklines in a raw lidar dataset and form the TIN model. Through a connected component analysis on the TIN model individual buildings are segmented. The final building boundary is formed by performing the Hough transform to the centers of the edge triangles in the TIN model. Rottensteiner and Briese (2002) use hierarchical robust interpolation (Kraus and Pfeifer, 1998) with a skew error distribution function to separate building and ground points. After applying morphological filters to the candidate building points, an initial building mask is obtained, which is then used to determine polyhedral building patches with a curvature-based segmentation process. The final individual building regions are found by a connected component analysis. For a comprehensive literature review, readers may refer to Vosselman et al. (2004) who present several techniques for segmenting aerial and terrestrial lidar point clouds into various classes and extracting different types of surfaces. To a similar level of extent, PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING J u l y 2007 805 Geomatics Engineering, School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907-2051 ([email protected]). Photogrammetric Engineering & Remote Sensing Vol. 73, No. 7, July 2007, pp. 805–812. 0099-1112/07/7307–0805/$3.00/0 © 2007 American Society for Photogrammetry and Remote Sensing 04-174 6/11/07 9:38 AM Page 805