  Remote sensing change detection (CD) is a pivotal technique that pinpoints
changes on a global scale based on multi-temporal images. With the recent
expansion of deep learning, supervised deep learning-based CD models have shown
satisfactory performance. However, CD sample labeling is very time-consuming as
it is densely labeled and requires expert knowledge. To alleviate this problem,
we introduce ChangeAnywhere, a novel CD sample generation method using the
semantic latent diffusion model and single-temporal images. Specifically,
ChangeAnywhere leverages the relative ease of acquiring large single-temporal
semantic datasets to generate large-scale, diverse, and semantically annotated
bi-temporal CD datasets. ChangeAnywhere captures the two essentials of CD
samples, i.e., change implies semantically different, and non-change implies
reasonable change under the same semantic constraints. We generated
ChangeAnywhere-100K, the largest synthesis CD dataset with 100,000 pairs of CD
samples based on the proposed method. The ChangeAnywhere-100K significantly
improved both zero-shot and few-shot performance on two CD benchmark datasets
for various deep learning-based CD models, as demonstrated by transfer
experiments. This paper delineates the enormous potential of ChangeAnywhere for
CD sample generation and demonstrates the subsequent enhancement of model
performance. Therefore, ChangeAnywhere offers a potent tool for remote sensing
CD. All codes and pre-trained models will be available at
https://github.com/tangkai-RS/ChangeAnywhere.
