Towards Temporal Change Explanations from Bi-Temporal Satellite Images
<|reference_start|>Towards Temporal Change Explanations from Bi-Temporal Satellite Images: Explaining temporal changes between satellite images taken at different times is important for urban planning and environmental monitoring. However, manual dataset construction for the task is costly, so human-AI collaboration is promissing. Toward the direction, in this paper, we investigate the ability of Large-scale Vision-Language Models (LVLMs) to explain temporal changes between satellite images. While LVLMs are known to generate good image captions, they receive only a single image as input. To deal with a par of satellite images as input, we propose three prompting methods. Through human evaluation, we found the effectiveness of our step-by-step reasoning based prompting.<|reference_end|>
@article{tsujimoto2024towards,
    title={Towards Temporal Change Explanations from Bi-Temporal Satellite Images},
    author={Ryo Tsujimoto, Hiroki Ouchi, Hidetaka Kamigaito, Taro Watanabe},
    journal={arXiv preprint arXiv:2407.09548},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09548},
    primaryClass={cs.CV cs.AI}
}