1,2,2,3,4,5- 1Department of Sustainable Development, Environmental Science and Engineering (SEED), KTH Royal Institute of Technology, Stockholm SE-100 44, Sweden (solmazkm@kth.se)
- 2Department of Physical Geography, Faculty of Science, Stockholm University, Sweden
- 3Polytechnic University of Coimbra, Rua da Misericórdia, Lagar dos Cortiços, S. Martinho do Bispo, 3045-093 Coimbra, Portugal
- 4Research Center for Natural Resources, Environment and Society (CERNAS), Polytechnic University of Coimbra, Bencanta, 3045-601 Coimbra, Portugal
- 5Department of Natural Resources, College of Agriculture and Natural Resources, Razi University, Kermanshah, Iran
Flood is the most common natural disaster in the world, and can have catastrophic impacts on human society and the environment, including infrastructure damage, agricultural losses, and casualties, resulting in widespread economic and social disruptions. In early studies, water body detection relied on on-the-spot investigation, hydrological models and common remote sensing techniques that face issues like slow processing and real-time delays. By addressing this challenges we propose a novel hybrid PoLSAR-metaheuristic-DL models and high-resolution remote sensing data to generate accurate and rapid flood mapping for one of the huge recent flood in France. Compared with standard synthetic aperture radars (SAR), polarimetric synthetic aperture radar (PolSAR) is an advanced technique of SAR remote sensing. So, by using polarimetric decomposition methods, features were extracted and feature selection problem, one of the most challenging, was solved by using metaheuristic techniques. The selected features fed into three deep learning-based segmentation models- U_Net_V3, Nested_UNet and Efficient_UNet. The reliability of the generated flood maps was evaluated using Accuracy, precision and recall metrics. Our experimental results indicate that Nested_UNet integrate with optimized PolSAR data achieves the highest segmentation performance, with an accuracy of 0.910, precision of 0.914, and recall of 0.909. These findings underscore the capability of Nested_UNet, demonstrates superior feature extraction abilities, making it a promising choice for real-time flood segmentation applications. Moreover, detecting the knowledge of flooded areas, officials can actively adopt steps to reduce the potential impact of flood, ensure the sustainable management of natural resources and mitigate flood impacts.
Keywords: Flood Segmentation, U_Net_V3, Nested_UNet, Efficient_UNet, PolSAR, Methaheuristis algorithms, France
How to cite: Khazaei Moughani, S., Kalantari, Z., Zou, L., Jaramillo, F., Santos Ferreira, C. S., and Khosravi, K.: Enhanced Flood Detection through Innovative Integration of PolSAR, Metaheuristic Optimization, and Deep Learning-Based Segmentation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7010, https://doi.org/10.5194/egusphere-egu26-7010, 2026.
