构建首个大规模灾变前后遥感图像与描述数据集，助力灾情智能分析。
RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Events
- 采集6.2万对灾前灾后遥感影像并配以人类级变化描述
- 支持多灾种（地震/洪水/火灾等）动态影响分析与视觉语言建模
- 适合遥感、AI灾备、跨模态理解方向研究者使用
遥感在灾害监测中至关重要，但现有数据集缺乏时间序列图像对和详尽文本标注。当前资源主要依赖单时相影像，无法捕捉灾害的动态演变过程。为此，我们提出遥感变化描述数据集（RSCC），包含62,351对灾前灾后图像（涵盖地震、洪水、野火等多种灾害），并配有丰富的人类级变化描述。RSCC弥合了遥感数据在时间和语义上的鸿沟，可有效支持视觉-语言模型在灾情感知双时相理解中的训练与评估。实验表明，该数据集能推动更精准、可解释且可扩展的遥感视觉-语言应用发展。代码与数据集已开源：https://github.com/Bili-Sakura/RSCC。
原文摘要 · Abstract (English)
Remote sensing is critical for disaster monitoring, yet existing datasets lack temporal image pairs and detailed textual annotations. While single-snapshot imagery dominates current resources, it fails to capture dynamic disaster impacts over time. To address this gap, we introduce the Remote Sensing Change Caption (RSCC) dataset, a large-scale benchmark comprising 62,351 pre-/post-disaster image pairs (spanning earthquakes, floods, wildfires, and more) paired with rich, human-like change captions. By bridging the temporal and semantic divide in remote sensing data, RSCC enables robust training and evaluation of vision-language models for disaster-aware bi-temporal understanding. Our results highlight RSCC's ability to facilitate detailed disaster-related analysis, paving the way for more accurate, interpretable, and scalable vision-language applications in remote sensing. Code and dataset are available at https://github.com/Bili-Sakura/RSCC.
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