Building Height Extraction from Monocular Off-Nadir Satellite Sensor Imagery

The extraction of building heights from monocular satellite sensor imagery poses a significant difficulty in remote sensing. Deep learning-based multi-task frameworks have recently emerged, showing potential in concurrently predicting 2D building shapes and heights from ortho-rectified satellite images. These techniques, however, have several limitations, including a lack of direct height markers, inherent uncertainties in height estimation, a heavy reliance on limited digital surface models, suboptimal integration of semantic-height feature, and inadequate depiction of the spatial relationships between buildings. To address these challenges, we develop a novel instance-level multi-task learning model that uses off-nadir imaging angles, roof-to-footprint as a proxy for height, and integrates certain geometric properties to increase height estimation accuracy. This method was validated using a self-constructed dataset, demonstrating significantly superior performance relative to two benchmarks.