Structure-aware deep learning network for building height estimation

• A novel dual-branch building height estimation network is proposed. • A structure-aware decoder is designed to capture the special features of buildings. • A gating mechanism is introduced to ensure the structural completeness of buildings. • Sentinel-1 and Sentinel-2 data are integrated to produce 10-m building height maps. Accurate building height information is essential for urban management and planning. However, most existing methods rely on general segmentation networks for building height estimation, often ignoring the structural characteristics of buildings. This paper proposes a novel structure-aware building height estimation (SBHE) model to address this limitation. The model is designed as a dual-branch architecture: one branch extracts building footprints from Sentinel-2 imagery, while the other estimates building heights from Sentinel-1 imagery. A structure-aware decoder and a gating mechanism are developed to integrate into SBHE to capture and account for the structural characteristics of buildings. Validation conducted in the Yangtze River Delta region of China demonstrates that SBHE achieved a more accurate building height map (RMSE = 4.62 m) than four existing methods (RMSE = 5.071 m, 7.148 m, RMSE = 10.16 m, and 13.41 m). Meanwhile, SBHE generated clearer building contours and better structural completeness. Thus, the proposed SBHE offers a robust tool for building height mapping. The source code of SBHE model can be available at: https://github.com/cheneason/SBHE-model .