HGDNet: A Height-Hierarchy Guided Dual-Decoder Network for Single View Building Extraction and Height Estimation

• URL: http://arxiv.org/abs/2308.05387v1
• Date: Thu, 10 Aug 2023 07:03:32 GMT
• Title: HGDNet: A Height-Hierarchy Guided Dual-Decoder Network for Single View Building Extraction and Height Estimation
• Authors: Chaoran Lu, Ningning Cao, Pan Zhang, Ting Liu, Baochai Peng, Guozhang Liu, Mengke Yuan, Sen Zhang, Simin Huang, Tao Wang
• Abstract summary: We propose a Height-hierarchy Guided Dual-decoder Network (HGDNet) to estimate building height. Under the guidance of synthesized discrete height-hierarchy nDSM, auxiliary height-hierarchical building extraction branch enhance the height estimation branch. Additional two-stage cascade architecture is adopted to achieve more accurate building extraction.
• Score: 13.09940764764909
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unifying the correlative single-view satellite image building extraction and height estimation tasks indicates a promising way to share representations and acquire generalist model for large-scale urban 3D reconstruction. However, the common spatial misalignment between building footprints and stereo-reconstructed nDSM height labels incurs degraded performance on both tasks. To address this issue, we propose a Height-hierarchy Guided Dual-decoder Network (HGDNet) to estimate building height. Under the guidance of synthesized discrete height-hierarchy nDSM, auxiliary height-hierarchical building extraction branch enhance the height estimation branch with implicit constraints, yielding an accuracy improvement of more than 6% on the DFC 2023 track2 dataset. Additional two-stage cascade architecture is adopted to achieve more accurate building extraction. Experiments on the DFC 2023 Track 2 dataset shows the superiority of the proposed method in building height estimation ({\delta}1:0.8012), instance extraction (AP50:0.7730), and the final average score 0.7871 ranks in the first place in test phase.

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