Copernicus卫星在灾害响应中的潜力：从Sentinel-1和Sentinel-2中获取建筑损毁信息
The Potential of Copernicus Satellites for Disaster Response: Retrieving Building Damage from Sentinel-1 and Sentinel-2
AI总结：
本文探讨了Copernicus计划的中分辨率地球观测图像在建筑损毁评估中的应用，引入了xBD-S12数据集，展示了在多种灾害场景中通过Sentinel-1和Sentinel-2图像能够有效检测和映射建筑损毁，尽管地面采样距离为10米。
摘要：
自然灾害要求快速评估损害以指导人道主义响应。本文研究了Copernicus计划的中分辨率地球观测图像是否能支持建筑损毁评估，补充了高分辨率图像，后者通常供应有限。我们引入了包含10,315对预灾和后灾图像的xBD-S12数据集，这些图像来自Sentinel-1和Sentinel-2，与已建立的xBD基准在时空上对齐。在一系列实验中，我们证明在许多灾害场景中，建筑损毁可以被检测和映射，尽管地面采样距离为10米。我们还发现，在该分辨率下，建筑复杂性对损毁映射似乎没有显著优势：更复杂的模型架构在泛化到未见过的灾害时表现不佳，而地理空间基础模型在实践中带来很少的好处。我们的结果表明，Copernicus图像是一种可行的数据源，可用于快速、大范围的损毁评估，并可能在高分辨率图像旁边发挥重要作用。我们发布了xBD-S12数据集、代码和训练好的模型，以支持进一步研究，网址为https://github.com/prs-eth/xbd-s12。
英文摘要：
Natural disasters demand rapid damage assessment to guide humanitarian response. Here, we investigate whether medium-resolution Earth observation images from the Copernicus program can support building damage assessment, complementing very-high resolution imagery with often limited availability. We introduce xBD-S12, a dataset of 10,315 pre- and post-disaster image pairs from both Sentinel-1 and Sentinel-2, spatially and temporally aligned with the established xBD benchmark. In a series of experiments, we demonstrate that building damage can be detected and mapped rather well in many disaster scenarios, despite the moderate 10$\,$m ground sampling distance. We also find that, for damage mapping at that resolution, architectural sophistication does not seem to bring much advantage: more complex model architectures tend to struggle with generalization to unseen disasters, and geospatial foundation models bring little practical benefit. Our results suggest that Copernicus images are a viable data source for rapid, wide-area damage assessment and could play an important role alongside VHR imagery. We release the xBD-S12 dataset, code, and trained models to support further research at https://github.com/prs-eth/xbd-s12 .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
