HGDNet: A Height-Hierarchy Guided Dual-Decoder Network for Single View
  Building Extraction and Height Estimation
Chaoran Lu Ningning Cao Pan Zhang Ting Liu Baochai Peng Guozhang Liu Mengke Yuan Sen Zhang Simin Huang Tao Wang
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.