ChangeChat: An Interactive Model for Remote Sensing Change Analysis via Multimodal Instruction Tuning
<|reference_start|>ChangeChat: An Interactive Model for Remote Sensing Change Analysis via Multimodal Instruction Tuning: Remote sensing (RS) change analysis is vital for monitoring Earth's dynamic processes by detecting alterations in images over time. Traditional change detection excels at identifying pixel-level changes but lacks the ability to contextualize these alterations. While recent advancements in change captioning offer natural language descriptions of changes, they do not support interactive, user-specific queries. To address these limitations, we introduce ChangeChat, the first bitemporal vision-language model (VLM) designed specifically for RS change analysis. ChangeChat utilizes multimodal instruction tuning, allowing it to handle complex queries such as change captioning, category-specific quantification, and change localization. To enhance the model's performance, we developed the ChangeChat-87k dataset, which was generated using a combination of rule-based methods and GPT-assisted techniques. Experiments show that ChangeChat offers a comprehensive, interactive solution for RS change analysis, achieving performance comparable to or even better than state-of-the-art (SOTA) methods on specific tasks, and significantly surpassing the latest general-domain model, GPT-4. Code and pre-trained weights are available at https://github.com/hanlinwu/ChangeChat.<|reference_end|>
@article{deng2024changechat:,
    title={ChangeChat: An Interactive Model for Remote Sensing Change Analysis via
  Multimodal Instruction Tuning},
    author={Pei Deng, Wenqian Zhou, Hanlin Wu},
    journal={arXiv preprint arXiv:2409.08582},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08582},
    primaryClass={cs.CV}
}