# An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images

arXiv: https://arxiv.org/abs/2507.08096 · HTML: https://mlsanity.com/paper/2507.08096/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Large-scale Building Height Retrieval from Single SAR Imagery based on Bounding Box Regression Networks](https://mlsanity.com/paper/2111.09460/) (2021)
- [Building Floorspace in China: A Dataset and Learning Pipeline](https://mlsanity.com/paper/2303.02230/) (2023)
- [A CNN regression model to estimate buildings height maps using Sentinel-1 SAR and Sentinel-2 MSI time series](https://mlsanity.com/paper/2307.01378/) (2023)
- [A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features](https://mlsanity.com/paper/2503.07230/) (2025)

## Abstract (arXiv)

Accurate estimation of building heights using very high resolution (VHR) synthetic aperture radar (SAR) imagery is crucial for various urban applications. This paper introduces a Deep Learning (DL)-based methodology for automated building height estimation from single VHR COSMO-SkyMed images: an object-based regression approach based on bounding box detection followed by height estimation. This model was trained and evaluated on a unique multi-continental dataset comprising eight geographically diverse cities across Europe, North and South America, and Asia, employing a cross-validation strategy to explicitly assess out-of-distribution (OOD) generalization. The results demonstrate highly promising performance, particularly on European cities where the model achieves a Mean Absolute Error (MAE) of approximately one building story (2.20 m in Munich), significantly outperforming recent state-of-the-art methods in similar OOD scenarios. Despite the increased variability observed when generalizing to cities in other continents, particularly in Asia with its distinct urban typologies and prevalence of high-rise structures, this study underscores the significant potential of DL for robust cross-city and cross-continental transfer learning in building height estimation from single VHR SAR data.