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.

翻译：统一相关的单视角卫星图像建筑提取与高度估计任务，为大规模城市三维重建中的表征共享及通用模型获取提供了有前景的途径。然而，建筑足迹与立体重建nDSM高度标签之间常见的空间错位会导致两项任务性能下降。为解决该问题，我们提出了一种高度层级引导的双解码器网络（HGDNet）用于建筑高度估计。在合成离散高度层级nDSM的引导下，辅助的高度层级建筑提取分支通过隐式约束增强高度估计分支，在DFC 2023 track2数据集上实现了超过6%的精度提升。此外，采用两阶段级联架构以实现更精确的建筑提取。在DFC 2023 Track 2数据集上的实验表明，该方法在建筑高度估计（δ1：0.8012）、实例提取（AP50：0.7730）方面具有优越性，最终平均得分0.7871在测试阶段排名第一。