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Automatic roof segmentation from airborne light detection and ranging (LiDAR) point cloud data is a key technology for building reconstruction and digital city modeling. In this article, we develop a novel region expansion based <italic>L</italic>0 gradient minimization algorithm for processing unordered point cloud data, and a two-stage global optimization method consisting of the <italic>L</italic>0 gradient minimization and graph cut for roof plane segmentation. First, we extract the LiDAR points of buildings from the original point cloud data with a deep learning based method and separate the points of the different buildings using Euclidean clustering to improve the processing efficiency. Second, region expansion based <italic>L</italic>0 gradient minimization is proposed, which is specially designed for roof plane segmentation from unordered point clouds. To fundamentally avoid the need for empirical parameter tuning in <italic>L</italic>0 gradient minimization, we propose a multistage coarse-to-fine segmentation process, which further improves the effect of the roof plane segmentation. Finally, graph cut is utilized to solve the jagged boundary and oversegmentation problems existing in the segmented roof planes and produce the segmentation results. We conducted comparative experiments on the Vaihingen and Hangzhou datasets. The experimental results show that the proposed approach significantly outperforms the current state-of-the-art approaches at least 6.7&#x0025; and 8.9&#x0025; in roof plane quality index in the Vaihingen and Hangzhou datasets, while showing superior robustness to different kinds of data.

Roof Plane Segmentation From LiDAR Point Cloud Data Using Region Expansion Based L0 Gradient Minimization and Graph Cut

Roof Plane Segmentation From LiDAR Point Cloud Data Using Region Expansion Based L₀ Gradient Minimization and Graph Cut