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

2025/07/10 by Babak Memar, L. Russo, Memar, Babak +5
Engineering · Environmental Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Remote Sensing and LiDAR Applications #Remote-Sensing Image Classification #Synthetic Aperture Radar (SAR) Applications and Techniques

paper · pdf · doi:10.48550/arxiv.2507.08096

openalex publication_date 2025/07/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features
• A CNN regression model to estimate buildings height maps using Sentinel-1 SAR and Sentinel-2 MSI time series
• Building Floorspace in China: A Dataset and Learning Pipeline
• Large-scale Building Height Retrieval from Single SAR Imagery based on Bounding Box Regression Networks
• Deep Residual Learning for Image Recognition
• U-Net: Convolutional Networks for Biomedical Image Segmentation

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