  While annotated images for change detection using satellite imagery are
scarce and costly to obtain, there is a wealth of unlabeled images being
generated every day. In order to leverage these data to learn an image
representation more adequate for change detection, we explore methods that
exploit the temporal consistency of Sentinel-2 times series to obtain a usable
self-supervised learning signal. For this, we build and make publicly available
(https://zenodo.org/record/4280482) the Sentinel-2 Multitemporal Cities Pairs
(S2MTCP) dataset, containing multitemporal image pairs from 1520 urban areas
worldwide. We test the results of multiple self-supervised learning methods for
pre-training models for change detection and apply it on a public change
detection dataset made of Sentinel-2 image pairs (OSCD).
