A new approach for roof segmentation from airborne LiDAR point clouds

Abstract

Accurate roof segmentation is one of the key steps for automatically constructing three-dimensional (3-D) building models. Building roofs can differ significantly in terms of their size, shape complexity, and number, rendering many existing airborne Light Detection And Ranging (LiDAR) roof segmentation methods ineffective. Thus, the applicability and precision of these methods need to be improved. For this purpose, this paper proposes a new roof segmentation method for airborne LiDAR point clouds. The proposed method integrates a novel region growing strategy and RANdom SAmple Consensus (RANSAC), applying these approaches to extract several reliable roof patches and then performing an iterative process to merge roof patches based on their parameters and the concept of inlier selection of RANSAC. Finally, unsegmented points and segmentation results are refined by voting in a local neighbourhood. The experimental results show that the proposed method can effectively segment the roofs of buildings with different complexity and sizes. As the basic evaluation primitive, the average segmentation correctness was found to be 95.67 and 97.85% when using a roof and a single point, respectively, which can provide reliable information for applications, such as 3-D building model reconstruction.