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

Zhenyuan Chen, Chenxi Wang, Ningyu Zhang, Feng Zhang

Keywords: Remote Sensing, VLM, Change Caption

TL;DR: We introduce the Remote Sensing Change Caption (RSCC) dataset, a large-scale benchmark comprising 62,315 pre-/post-disaster image pairs with detailed change captions.

Abstract: 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.

Dataset URL: https://huggingface.co/datasets/BiliSakura/RSCC

Code URL: https://github.com/Bili-Sakura/RSCC

Primary Area: Datasets & Benchmarks for applications in language modeling and vision language modeling

Submission Number: 280