STURM-Flood: a curated dataset for deep learning-based flood extent mapping leveraging Sentinel-1 and Sentinel-2 imagery
Nicla Notarangelo, Charlotte Wirion, Frankwin van Winsen
Abstract
Flooding is a major global natural disaster exacerbated by climate change and urbanization. Timely assessment and mapping of inundations are crucial for preventive and emergency measures, driving the demand for curated global geospatial data to implement novel algorithms. This study introduces the STURM-Flood dataset, a high-quality, open-access, and DL-ready resource for flood extent mapping using Sentinel-1 and Sentinel-2 satellite imagery, combined with ground-truth data from the Copernicus Emergency Management Service. The dataset encompasses 21,602 Sentinel-1 tiles and 2,675 Sentinel-2 tiles, each measuring 128 × 128 pixels at 10 m resolution, alongside corresponding water masks covering 60 flood events globally. Two U-Net models evaluated the dataset: Sentinel-1 achieved 83.61% test accuracy and 0.8327 weighted F1-score, while Sentinel-2 yielded 92.75% test accuracy and 0.9243 weighted F1-score. These results underscore the dataset’s potential in developing robust models for water extent mapping. STURM-Flood dataset aims to provide a valuable resource for research and development in flood mapping and disaster management. Future research could focus on expanding and refining different approaches and data sources for broader applications. The reference data and code are openly available in the Zenodo repository https://doi.org/10.5281/zenodo.12748983 and GitHub repository https://github.com/STURM-WEO/STURM-Flood.