  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.
