Flood Impact Risk Mapping in Settlement Areas from a 3D Perspective: A Case Study of Hurricane Matthew

This study investigates an approach to map 3D flood map (i.e., floodwater extent and depth) using UAV high resolution imagery and LiDAR for Hurricane Matthew. We utilize a deep learning approach to map flooded areas from post event UAV images, and then employ spatial statistics to estimate the water depth of the flooded areas leveraging on the DEM. Afterward, an auxiliary dataset is combined with the generated flood depth result to map and analyze flood impact risk in settlement areas within the study area. Our result showed that settlement areas in Grifton exhibit different risk levels from a 3D flood depth perspective. This information could significantly enhance near real-time emergency response strategies, as well as future mitigation initiatives.