DeepFlood: Enhancing large scale Flood Detection and Mapping using PolSAR, Metaheuristic, and Deep Learning Algorithms
January 2024 – December 2025
DeepFlood aims to develop novel hybrid models and flood maps with water depth information to support real-time decision-making and present them to the Swedish and international scientific society and the stakeholders’ community. The research will be helpful for improving our fundamental understanding of SAR-based flood mapping by developing novel hybrid PolSAR-metaheuristic-DL models.
Precise and fast flood mapping will help water resources managers, stakeholders, and decision-makers in mitigating the impact of floods. Rapid detection of flooded areas and information about water depth are critical for assisting flood responders, e.g., operation specialists, local and state authorities, etc., and increasing preparedness of the broader community through actions such as home risk mitigation and evacuation planning.
This project seeks to fill current knowledge gaps in flood management by enabling accurate and rapid flood mapping and providing water depth information using novel hybrid PolSAR-metaheuristic-DL models and high-resolution remote sensing data. It will also advance flood detection and support notification systems by identifying 1) bands and polarizations that contain the most information for detecting flooded areas in different land covers; 2) the most effective PolSAR features in each band for flood mapping; 3) whether the most informative PolSAR features are the same for different land covers; and 4) which of the widely used metaheuristic and DL models are most efficient for detecting flooded areas and estimate water depth.
The researchers in the team represent KTH Royal Institute of Technology and Stockholm University.
Assistant Professor at Stockholm University, Co-PI: DeepFlood - Enhancing large scale Flood Detection and Mapping using PolSAR, Metaheuristic, and Deep Learning Algorithms, Former Main supervisor: Deep Wetlands, Digital Futures Faculty08-16 47 71
Researcher, Division of Water and Environmental Engineering at the Department of Sustainable Development, Environmental Science and Engineering (SEED), KTH, Co-PI of project DeepFlood: Enhancing large scale Flood Detection and Mapping using PolSAR, Metaheuristic, and Deep Learning Algorithms, Digital Futures Faculty+46 8 790 86 33