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Yanjiao Song, Bowen Cai, Timo Balz, Zhenfeng Shao, Neema Simon Sumari, James Magidi, Walter Musakwa
· 1 min read
ResearcharXiv cs.CV
The First Assessment of PhiSat-2 Imagery for Monocular Building Height Estimation
arXiv:2603.29245v4 Announce Type: replace
Abstract: PhiSat-2 provides multispectral observations at a 4.75m spatial resolution across geographically diverse scenes, offering a new source of remote sensing imagery for monocular building-height estimation. To comprehensively assess its utility, this study introduces the PhiSat-2--Height Dataset (PHDataset) and the Two-Stream Ordinal Network (TSONet). PHDataset integrates global PhiSat-2 imagery with open building-height references and contains 9475 co-registered patch pairs from 26 cities worldwide. In TSONet, multiscale decoupled height representation (MDHR) separates adaptive bin generation from spatial assignment, while footprint-conditioned height reconstruction (FCHR) incorporates footprint guidance at feature and reconstruction levels. Under city-disjoint evaluation, TSONet reduces building RMSE and MAE by 3.64% and 4.49%, respectively, relative to the most accurate comparison method originally designed for building-height estimation, while maintaining similar height-derived footprint IoU. Ablations support the combined design for height accuracy and footprint consistency. PhiSat-2 reduces building RMSE by 3.73% and 6.05% relative to the evaluated Sentinel-2 and Landsat 8 configurations, respectively, with all seven bands providing the lowest building-height errors among its tested band subsets. LiDAR-based testing supports PHDataset-style supervision in a held-out city, while selected patch comparisons show finer urban height patterns than coarser global products. These findings highlight the value of PhiSat-2 spatial-spectral information for urban building height mapping, with PHDataset and TSONet providing a foundation for further development.
Original source
This story was published by arXiv cs.CV and written by Yanjiao Song, Bowen Cai, Timo Balz, Zhenfeng Shao, Neema Simon Sumari, James Magidi, Walter Musakwa. SyncAI.news shows a preview; the complete article is on the publisher's site.
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