  Recent advancements in computer vision and deep learning techniques have
facilitated notable progress in scene understanding, thereby assisting rescue
teams in achieving precise damage assessment. In this paper, we present
RescueNet, a meticulously curated 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. RescueNet comprises post-disaster images collected after
Hurricane Michael, obtained using Unmanned Aerial Vehicles (UAVs) from multiple
impacted regions. The uniqueness of RescueNet lies in its provision of
high-resolution post-disaster imagery, accompanied by comprehensive annotations
for each image. Unlike existing datasets that offer annotations limited to
specific scene elements such as buildings, RescueNet provides pixel-level
annotations for all classes, including buildings, roads, pools, trees, and
more. Furthermore, we evaluate the utility of the dataset by implementing
state-of-the-art segmentation models on RescueNet, demonstrating its value in
enhancing existing methodologies for natural disaster damage assessment.
