Multi-Source Data Fusion for Flood Mapping in Response to Climate Change

In the context of escalating climate change impacts, precise flood mapping has become crucial for effective disaster response and management. We address this challenge by developing a deep learning model based on the U-Net architecture, specifically tailored for rapid and accurate flood extent mapping using Synthetic Aperture Radar (SAR) data from the Copernicus Sentinel-1 mission published as part of IEEE 2024 GRSS data fusion challenge. Our approach leverages the unique capabilities of U-Net for detailed image segmentation, combined with SAR imagery, digital elevation models, land-use patterns, and historical water presence data to differentiate between water and non-water surfaces accurately. The model was rigorously trained and validated across a comprehensive dataset representing a variety of geographic conditions and flood events. It demonstrated significant advancements over traditional flood mapping techniques, achieving an overall accuracy of 95% and an F1-score of 0.7. These metrics underscore the model's effectiveness in accurately classifying flooded areas. Furthermore, the model exhibits excellent scalability and adaptability, offering potential applications beyond the initial flood mapping scope to other environmental and disaster management scenarios. Our findings not only contribute to the improvement of flood response strategies but also underscore the potential of advanced machine learning techniques in enhancing the accuracy and efficiency of environmental monitoring in the face of global climate challenges. This research represents a significant step forward in the application of deep learning to hydrological extremes, providing a robust tool for emergency response planners and climate scientists alike.