RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Events

Ningyu Zhang (Zhejiang University) · Zhenyuan Chen (Nankai University) · Chenxi Wang (Mohamed bin Zayed University of Artificial Intelligence) · Feng Zhang (Zhejiang University)

benchmark datasetbi-temporal understandingchange captionsdisaster monitoringdisaster-related analysisdynamic disaster impactshuman-like descriptionsinterpretable applicationspre-/post-disaster imagesremote sensingremote sensing change captionscalable applicationstemporal image pairstextual annotationsvision-language models

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.