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.

翻译：近期计算机视觉与深度学习技术的进步推动了场景理解领域的显著发展，从而协助救援团队实现精确的损害评估。本文提出RescueNet——一个精心构建的高分辨率灾后数据集，包含详细的分类与语义分割标注。该数据集旨在促进自然灾害发生后对场景的全面理解。RescueNet包含飓风迈克尔后使用无人机从多个受影响区域采集的灾后影像。其独特性在于提供高分辨率灾后影像及每张影像的完整标注。不同于现有数据集仅对建筑等特定场景元素进行标注，RescueNet对所有类别（包括建筑、道路、池塘、树木等）提供像素级标注。此外，我们通过在RescueNet上部署最先进的分割模型评估该数据集的实用性，证明其增强现有自然灾害损害评估方法的价值。