RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Events
This dataset addresses a gap in disaster monitoring for remote sensing researchers and practitioners, though it is incremental as it builds on existing vision-language methods by providing new data.
The authors tackled the lack of temporal image pairs and detailed textual annotations in remote sensing for disaster monitoring by introducing the RSCC dataset, which includes 62,315 pre-/post-disaster image pairs with human-like change captions, enabling robust training and evaluation of vision-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 62,315 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.