Seed point set-based building roof extraction from airborne LiDAR point clouds using a top-down strategy

Building roof extraction from airborne laser scanning point clouds is significant for building modeling. The common method adopts a bottom-up strategy which requires a ground filtering process first, and the subsequent process of region growing based on a single seed point easily causes oversegmentation problem. This paper proposes a novel method to extract roofs. A top-down strategy based on cloth simulation is first used to detect seed point sets with semantic information; then, the roof seed points are extracted instead of a single seed point for region-growing segmentation. The proposed method is validated by three point cloud datasets that contain different types of roof and building footprints. The results show that the top-down strategy directly extracts roof seed point sets, most roofs are extracted by the region-growing algorithm based on the seed point set, and the total errors of roof extraction in the test areas are 0.65%, 1.07%, and 1.45%. The proposed method simplifies the workflow of roof extraction, reduces oversegmentation, and determines roofs in advance based on the semantic seed point set, which suggests a practical solution for rapid roof extraction. •A top-down strategy is presented for extracting roofs from airborne LiDAR point clouds.•The proposed strategy avoids ground filtering and simplifies the workflow of roof extraction.•The cloth simulation algorithm detects seed points.•The combination of roughness and connect-component labeling determines roof seed sets.•The proposed seed point set-based region-growing method reduces oversegmentation.