Automated segmentation of LiDAR point clouds for building rooftop extraction

Abstract

LIDAR (Light Detection and Ranging) is an especially effective tool for acquiring geo-referenced point clouds of urban site. Accurate extraction of elevated features such as building rooftops is vitally important in various applications. However, it is still challenging to determine an accurate rooftop contour from the irregularly distributed LIDAR point clouds. In this paper an efficient LIDAR segmentation method is presented in order to achieve automated rooftop extraction. First, we apply a voxel-based upward growing algorithm that filters out the ground points from the raw point cloud scenes. Second, we employ a Euclidean based clustering method on non-ground points by making use of nearest neighbors. Then we introduce RANSAC (RANdom SAmple Consensus) technique to estimate primitive planes for fitting rooftop facets. Finally, we use concave hull and L 0 regularization to determine the rooftop contour. Accurate experimental results demonstrate the validity of our segmentation method for rooftop extraction.