U-Net-based waterline segmentation for flood disaster response

Floods are among the most destructive natural disasters, causing human casualties and severe economic damage. Effective monitoring of flooded areas requires automated systems capable of real-time perception and decision-making. This paper proposes a water surface segmentation model based on the U-Net architecture, trained entirely on synthetic data generated in the Gazebo simulator with variations in shoreline shape, water level, and surface color. The model demonstrated high segmentation accuracy on a test set (Dice coefficient = 0.9663, IoU = 0.9530) and maintained robustness across diverse scenarios, ranging from natural lakes to complex urban environments. When integrated into a UAV navigation algorithm, the system enabled real-time flight along detected flood boundaries. These results confirm feasibility of applying U-Net-based segmentation for UAV-assisted flood monitoring and search-and-rescue operations. Future work focuses on validating the approach with real-world data and adapting the network to resource-constrained onboard platforms