Urban building roof segmentation from airborne lidar point clouds

Abstract

This article presents a new approach to segmenting building rooftops from airborne lidar point clouds. A progressive morphological filter technique is first applied for separation between ground and non-ground points. For the non-ground points, a region-growing algorithm based on a plane-fitting technique is used to separate building points from vegetation points. Then, an adaptive Random Sample Consensus (RANSAC) algorithm based on a grid structure is developed to improve the probability of selecting an uncontained sample from the localized sampling. The distance, standard deviation and normal vector are integrated to keep topological consistency among building rooftop patches during building rooftop segmentation. Finally, the remaining points are mapped on to the extracted planes by a post-processing technique to improve the segmentation accuracy. The results for buildings with different roof complexities are presented and evaluated.