RANSAC-based segmentation for building roof face detection in LiDAR point cloud

Abstract

This work proposes a method for segmenting the roof planes of buildings in Light Detection and Ranging (LiDAR) data. First, a preprocessing of the point cloud is performed to separate the points belonging to each building. The RANdom SAmple Consensus (RANSAC) method is then used in each building region to identify sets of coplanar points belonging to the roof faces. Finally, planar segments representing the same roof face are connected to minimize the fragmentation that may occur in the previous step. This requires the use of techniques for analyzing the continuity of adjacent planar segments. Although several thresholds are required, they can be predetermined or adapted, thus avoiding their modification by an operator in each application of the method. The results show that the proposed method functions appropriately, rarely failing in regions affected by local structures such as trees and antennas. Consequently, average rates higher than 90% were obtained for completeness and correction.