Monocular Building Height Estimation from PhiSat-2 Imagery: Dataset and Method
Abstract
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 and patch-level comparison with publicly available building height products 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.
Cite
@article{arxiv.2603.29245,
title = {Monocular Building Height Estimation from PhiSat-2 Imagery: Dataset and Method},
author = {Yanjiao Song and Bowen Cai and Timo Balz and Zhenfeng Shao and Neema Simon Sumari and James Magidi and Walter Musakwa},
journal= {arXiv preprint arXiv:2603.29245},
year = {2026}
}