Rescuenet: A High Resolution Uav Semantic Segmentation Dataset For Natural Disaster Damage Assessment

This paper presents RescueNet, a high-resolution post-disaster dataset that includes detailed classification and semantic segmentation annotations. This dataset aims to facilitate comprehensive scene understanding in the aftermath of natural disasters, and is comprised of post-disaster images collected after Hurricane Michael, obtained using Unmanned Aerial Vehicles (UAVs). RescueNet provides pixel-level annotations for all classes, including buildings, roads, pools, trees, and more. The utility of the dataset is evaluated by implementing state-of-the-art segmentation models on RescueNet, demonstrating its value in enhancing existing methodologies for natural disaster damage assessment.