How high are we? Large-scale building height estimation at 10 m using Sentinel-1 SAR and Sentinel-2 MSI time series

Accurate building height estimation is essential to support urbanization monitoring, environmental impact analysis and sustainable urban planning. However, conducting large-scale building height estimation remains a significant challenge. While deep learning (DL) has proven effective for large-scale mapping tasks, there is a lack of advanced DL models specifically tailored for height estimation, particularly when using open-source Earth observation data. In this study, we propose T-SwinUNet, an advanced DL model for large-scale building height estimation leveraging Sentinel-1 SAR and Sentinel-2 multispectral time series. T-SwinUNet model contains a feature extractor with local/global feature comprehension capabilities, a temporal attention module to learn the correlation between constant and variable features of building objects over time and an efficient multitask decoder to predict building height at 10 m spatial resolution. The model is trained and evaluated on data from the Netherlands, Switzerland, Estonia, and Germany, and its generalizability is evaluated on an out-of-distribution (OOD) test set from ten additional cities from other European countries. Our study incorporates extensive model evaluations, ablation experiments, and comparisons with established models. T-SwinUNet predicts building height with a Root Mean Square Error (RMSE) of 1.89 m, outperforming state-of-the-art models at 10 m spatial resolution. Its strong generalization to the OOD test set (RMSE of 3.2 m) underscores its potential for low-cost building height estimation across Europe, with future scalability to other regions. Furthermore, the assessment at 100 m resolution reveals that T-SwinUNet (0.29 m RMSE, 0.75 $R^2$) also outperformed the global building height product GHSL-Built-H R2023A product(0.56 m RMSE and 0.37 $R^2$). Our implementation is available at: https://github.com/RituYadav92/Building-Height-Estimation.