  Change detection, a prominent research area in remote sensing, is pivotal in
observing and analyzing surface transformations. Despite significant
advancements achieved through deep learning-based methods, executing
high-precision change detection in spatio-temporally complex remote sensing
scenarios still presents a substantial challenge. The recent emergence of
foundation models, with their powerful universality and generalization
capabilities, offers potential solutions. However, bridging the gap of data and
tasks remains a significant obstacle. In this paper, we introduce Time
Travelling Pixels (TTP), a novel approach that integrates the latent knowledge
of the SAM foundation model into change detection. This method effectively
addresses the domain shift in general knowledge transfer and the challenge of
expressing homogeneous and heterogeneous characteristics of multi-temporal
images. The state-of-the-art results obtained on the LEVIR-CD underscore the
efficacy of the TTP. The Code is available at \url{https://kychen.me/TTP}.
