用新卫星影像实现高精度单目建筑高度估计，效果优于现有方法。

Monocular building height estimation from optical imagery is important for characterizing urban vertical structure, yet remains challenging due to the heterogeneity of urban building morphology and the indirect relationship between optical image appearance and building height. The recently launched PhiSat-2 satellite provides a promising open-access data source for this task, with 4.75m spatial resolution and seven multispectral bands spanning the visible to near-infrared range. However, its suitability for monocular building height estimation has not been systematically assessed. This study presents an initial open-reference assessment of PhiSat-2 imagery for this task by constructing a PhiSat-2--Height Dataset (PHDataset) and proposing a Two-Stream Ordinal Network (TSONet). PHDataset integrates global PhiSat-2 imagery with open building-height references and contains 9,475 co-registered patch pairs from 26 cities worldwide. TSONet jointly learns dense height estimation and auxiliary footprint prediction, using footprint-aware structural guidance and ordinal height modeling to better exploit PhiSat-2 spatial--spectral information. Specifically, a Cross-Stream Exchange Module (CSEM) enables adaptive interaction between the height and footprint streams, while a Feature-Enhanced Bin Refinement (FEBR) module performs coarse-to-fine ordinal query refinement with multi-level features. Experiments on PHDataset show that TSONet outperforms representative competing methods, reducing MAE and RMSE by over 13.2% and 9.7%, respectively, while improving IoU and F1-score by over 14.0% and 10.1%. Additional analyses further indicate that PhiSat-2 imagery contains useful spatial--spectral cues for monocular building height estimation at an intermediate spatial resolution.