Domain Knowledge-Aware Remote Sensing Foundation Model for Flood Detection in Multi-Spectral Imagery

Obtaining accurate and timely flood information is crucial for effective disaster management and response. To address the limitations of existing methods in terms of accuracy and model robustness, this research significantly improves the accuracy and stability of flood detection by integrating domain knowledge into the Remote Sensing Foundation Model (RSFM). Specifically, we employ advanced RSFM to focus on extracting spatial texture features from images after super-resolution. The Automatic Water Extraction Index (AWEI) is introduced to leverage spectral information from multi-spectral imagery, while model fusion techniques further enhance the accuracy of segmentation results. Moreover, we incorporate prior knowledge such as land use products and Digital Elevation Models (DEM) for knowledge rules-driven post-processing, refining the final flood detection results. Experimental results demonstrate that our approach achieve the second-place ranking in the 2024 IEEE GRSS Data Fusion Contest (DFC) Track 2 test phase (F1: 88.25%), highlighting the effectiveness and competitiveness of our method.