MULTI-HEAD ENCODER-DECODER DEEP LEARNING ARCHITECTURE FOR FLOOD SEGMENTATION AND MAPPING THROUGH MULTI-SENSOR DATA FUSION

Effective disaster management and response require accurate and timely mapping of floodwater extent. Optical images facilitate easier flood identification, but their limitation with cloud cover makes them suitable mainly for post-flood analysis. SAR data, with its ability to penetrate clouds, offers advantages in flood scenarios. This study introduces a unique strategy by merging SAR data and UAV optical images, bridging the spatial-temporal gap between spaceborne and ground-based observations. UAVs provide precise details crucial for calibrating and validating flood routing models. The paper also proposes a deep learning-based approach for flood mapping through an efficient fusion of SAR and optical RGB imagery, contributing to enhanced disaster monitoring and response capabilities.