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Recent advancements in computer vision and deep learning techniques havefacilitated notable progress in scene understanding, thereby assisting rescueteams in achieving precise damage assessment. In this paper, we presentRescueNet, a meticulously curated high-resolution post-disaster dataset thatincludes detailed classification and semantic segmentation annotations. Thisdataset aims to facilitate comprehensive scene understanding in the aftermathof natural disasters. RescueNet comprises post-disaster images collected afterHurricane Michael, obtained using Unmanned Aerial Vehicles (UAVs) from multipleimpacted regions. The uniqueness of RescueNet lies in its provision ofhigh-resolution post-disaster imagery, accompanied by comprehensive annotationsfor each image. Unlike existing datasets that offer annotations limited tospecific scene elements such as buildings, RescueNet provides pixel-levelannotations for all classes, including buildings, roads, pools, trees, andmore. Furthermore, we evaluate the utility of the dataset by implementingstate-of-the-art segmentation models on RescueNet, demonstrating its value inenhancing existing methodologies for natural disaster damage assessment.

RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment