Urban building footprints extraction and heights estimation from high-resolution spaceborne remote sensing imagery using a CNN-Transformer network

High-resolution multispectral remote sensing imagery for accurate building footprint extraction and reliable height estimation often faces numerous challenges in complex urban environments with diverse building structures, heterogeneous shadow patterns, and widespread occlusions. This study proposes a sparse edge-aware convolutional-transformer neural network model, SECT-Net, for precise extraction of building footprints and their cast shadows from high-resolution Jilin-1 multispectral images covering Shanghai, China. A shadow-based height estimation workflow was then developed to characterize building heights from their shadow lengths. Results show that the SECT-Net achieves high performance in building footprint extraction, with an IoU of 77.81%, an F1 score of 87.52%, and an overall accuracy of 97.15%, respectively. Building heights of more than 750,000 buildings were estimated across the entire Shanghai, with R² = 0.74 and RMSE = 5.67 m. A slight systematic underestimation of building heights is attributed to occlusions of high-rise buildings and the disruption of vegetation in residential communities in dense urban central areas. This study demonstrates that the proposed SECT-Net can accurately and precisely extract building footprints from high-resolution spaceborne remotely sensed images. The estimated building heights provide a reliable underpinning for urban morphology analysis and building monitoring in urban planning and scientific management.