DeepFlood: Enhancing large scale flood detection and mapping using polsar, metaheuristic, and deep learning algorithms
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 wider community through actions such as home risk mitigation and evacuation planning.
This project aims to addressing 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:
- bands and polarizations that contain the most information for detecting flooded areas in different land covers;
- the most effective PolSAR features in each band for flood mapping;
- whether the most informative PolSAR features are the same for different land covers; and
- which of the widely used metaheuristic and DL models are most efficient for detecting flooded areas and estimate water depth.
Project period
2024–2026
Funding
Contact persons
Carla Ferreira, Polytechnic Institute of Coimbra, Portugal
Other SATORI participants
