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 .

翻译：自然灾害亟需快速灾害评估以指导人道救援行动。本研究探讨了哥白尼计划中分辨率地球观测影像能否支持建筑损毁评估，以补充空间分辨率极高但可用性有限的影像资源。我们构建了xBD-S12数据集，包含来自哨兵1号与哨兵2号的10,315对灾前/灾后影像，并与现有xBD基准数据集实现时空对齐。系列实验表明，尽管地面采样距离仅为10米，但在多种灾害场景中仍能较好地检测与绘制建筑损毁。研究同时发现，在该分辨率下的损毁制图任务中，架构复杂性并未带来显著优势：复杂模型架构在泛化至未见灾害时表现不佳，地理空间基础模型的实际增益有限。实验结果表明，哥白尼影像可作为快速大范围灾害评估的可行数据源，与甚高分辨率影像形成互补。我们已开源xBD-S12数据集、代码及预训练模型，详见https://github.com/prs-eth/xbd-s12，以推动后续研究。