  Remote sensing change detection, identifying changes between scenes of the
same location, is an active area of research with a broad range of
applications. Recent advances in multimodal self-supervised pretraining have
resulted in state-of-the-art methods which surpass vision models trained solely
on optical imagery. In the remote sensing field, there is a wealth of
overlapping 2D and 3D modalities which can be exploited to supervise
representation learning in vision models. In this paper we propose Contrastive
Surface-Image Pretraining (CSIP) for joint learning using optical RGB and above
ground level (AGL) map pairs. We then evaluate these pretrained models on
several building segmentation and change detection datasets to show that our
method does, in fact, extract features relevant to downstream applications
where natural and artificial surface information is relevant.
