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Monocular Building Height Estimation from PhiSat-2 Imagery: Dataset and Method

Yanjiao Song, Bowen Cai, Timo Balz, Zhenfeng Shao, Neema Simon Sumari, James Magidi, Walter Musakwa

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.

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.

Paper Structure

This paper contains 22 sections, 28 equations, 11 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Materials
  3. PhiSat-2 imagery
  4. Building height labels
  5. Dataset construction
  6. Method
  7. Overall structure
  8. Cross-Stream Exchange Module
  9. Feature-enhanced bin refinement
  10. Multi-task objective
  11. Experiments and results
  12. Metrics
  13. Implementation details
  14. Comparison with state-of-the-art methods
  15. Ablation experiments for modules
  16. ...and 7 more sections

Figures (11)

  • Figure 1: The spatial distribution and PhiSat-2 imagery examples of chosen scene locations.
  • Figure 2: Overall structure of the proposed Two-Stream Ordinal Network (TSONet).
  • Figure 3: The structure of Cross-Stream Exchange Module (CSEM).
  • Figure 4: The structure of Feature-Enhanced Bin Refinement (FEBR).
  • Figure 5: Qualitative results of comparison experiments.
  • ...and 6 more figures