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

Abstract : 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.
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https://hal.inria.fr/hal-03174470
Contributor : Nicolas Mellado <>
Submitted on : Friday, March 19, 2021 - 11:17:55 AM
Last modification on : Saturday, March 20, 2021 - 3:32:28 AM

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Jie Shao, Wuming Zhang, Aojie Shen, Nicolas Mellado, Shangshu Cai, et al.. Seed point set-based building roof extraction from airborne LiDAR point clouds using a top-down strategy. Automation in Construction, Elsevier, 2021, 126, pp.103660. ⟨10.1016/j.autcon.2021.103660⟩. ⟨hal-03174470⟩

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