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The authors introduce PHDataset, a new dataset of 9,475 co-registered image-label pairs from PhiSat-2 imagery across 26 cities, designed for monocular building height estimation. They also propose TSONet, a two-stream network that jointly models footprint segmentation and height estimation using a Cross-Stream Exchange Module (CSEM) and Feature-Enhanced Bin Refinement (FEBR). Experiments demonstrate that TSONet outperforms existing methods on PHDataset, achieving significant reductions in MAE and RMSE, and improvements in IoU and F1-score, highlighting the potential of PhiSat-2 data for this task.
Publicly available satellite imagery can now estimate building heights with state-of-the-art accuracy thanks to a new dataset and network architecture designed for the task.
Monocular building height estimation from optical imagery is important for urban morphology characterization but remains challenging due to ambiguous height cues, large inter-city variations in building morphology, and the long-tailed distribution of building heights. PhiSat-2 is a promising open-access data source for this task because of its global coverage, 4.75 m spatial resolution, and seven-band spectral observations, yet its potential has not been systematically evaluated. To address this gap, we construct a PhiSat-2-Height dataset (PHDataset) and propose a Two-Stream Ordinal Network (TSONet). PHDataset contains 9,475 co-registered image-label patch pairs from 26 cities worldwide. TSONet jointly models footprint segmentation and height estimation, and introduces a Cross-Stream Exchange Module (CSEM) and a Feature-Enhanced Bin Refinement (FEBR) module for footprint-aware feature interaction and ordinal height refinement. Experiments on PHDataset show that TSONet achieves the best overall performance, reducing MAE and RMSE by 13.2% and 9.7%, and improving IoU and F1-score by 14.0% and 10.1% over the strongest competing results. Ablation studies further verify the effectiveness of CSEM, FEBR, and the joint use of ordinal regression and footprint assistance. Additional analyses indicate that PhiSat-2 benefits monocular building height estimation through its balanced combination of building-relevant spatial detail and multispectral observations. Overall, this study confirms the potential of PhiSat-2 for monocular building height estimation and provides a dedicated dataset and an effective method for future research.
