# The First Assessment of PhiSat-2 Imagery for Monocular Building Height Estimation

arXiv: https://arxiv.org/abs/2603.29245 · HTML: https://mlsanity.com/paper/2603.29245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 17 of 17)

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- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [Deeper Depth Prediction with Fully Convolutional Residual Networks](https://mlsanity.com/paper/1606.00373/) (2016)
- [From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation](https://mlsanity.com/paper/1907.10326/) (2019)
- [LocalBins: Improving Depth Estimation by Learning Local Distributions](https://mlsanity.com/paper/2203.15132/) (2022)
- [IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network](https://mlsanity.com/paper/1802.10249/) (2018)
- [GlobalBuildingAtlas: An Open Global and Complete Dataset of Building Polygons, Heights and LoD1 3D Models](https://mlsanity.com/paper/2506.04106/) (2025)
- [Height estimation from single aerial images using a deep ordinal regression network](https://mlsanity.com/paper/2006.02801/) (2020)
- [Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correction](https://mlsanity.com/paper/2505.06905/) (2025)
- [A global product of fine-scale urban building height based on spaceborne lidar](https://mlsanity.com/paper/2310.14355/) (2023)
- [Reconstructing Building Height from Spaceborne TomoSAR Point Clouds Using a Dual-Topology Network](https://mlsanity.com/paper/2601.00658/) (2026)

## Abstract (arXiv)

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.