3D-Building Reconstruction with Different Height Levels from Airborne LiDAR Data

Kurzfassung

More than 50% of the world population inhabit in urban or suburban areas, thus detailed and upto-date building information is of great importance to every resident, government agencies, and private companies (utilities, real estates and etc.). 3D city models become increasingly important and necessary for supporting numerous applications such as crisis management, urban planning and so on (Verma u. a. [2006]). As LiDAR (light detection and ranging) considers a superior technology for 3D data acquisition from Earth’s surface and delivers point clouds with high quality. This research is conducted by merely utilizing LiDAR data of an interested area without another data sources such as a cadastral map or photogrammetric aerial images. In this thesis, a developed automated data-driven approach is proposed 3D building models from airborne LiDAR data starting from segmentation of the point cloud, extracting roof patches and ending with regularization of the patches boundary to extract 3D model. Unlike photogrammetry, it is well known that LiDAR data does not record exact position of the edges of buildings. So the first challenge in this research is to extract a relatively accurate building boundaries from 3D point clouds LiDAR data and obtained results are evaluated by comparing with footprints in the cadastral map. The second challenge is to deal with step edges among the adjacent flat roof patches even with the small height differences and solve overlapping problem in 3D space. For evaluation, the methodology is applied on another point cloud with different resolution. Also the extracted building polygons are compared with reference building polygons from cadastral map using a PoLiS metric based on polygon comparison (Avbelj u. a. [2015]). In addition, the area of individual roof patches and the area of whole footprints are computed and compared with their correspondences from cadastral map. Furthermore, RMSE distances (root mean square estimate) from raw LiDAR point clouds to the estimated 3D roof planes are computed.