A DATA DRIVEN METHOD FOR FLAT ROOF BUILDING RECONSTRUCTION FROM LiDAR POINT CLOUDS
Keywords: 3D Building Model, Segmentation, Edge Points Detection, Line Approximation
Abstract. 3D building modeling is one of the most important applications in photogrammetry and remote sensing. Airborne LiDAR (Light Detection And Ranging) is one of the primary information sources for building modeling. In this paper, a new data-driven method is proposed for 3D building modeling of flat roofs. First, roof segmentation is implemented using region growing method. The distance between roof points and the height difference of the points are utilized in this step. Next, the building edge points are detected using a new method that employs grid data, and then roof lines are regularized using the straight line approximation. The centroid point and direction for each line are estimated in this step. Finally, 3D model is reconstructed by integrating the roof and wall models. In the end, a qualitative and quantitative assessment of the proposed method is implemented. The results show that the proposed method could successfully model the flat roof buildings using LiDAR point cloud automatically.