  Contrastive learning methods have significantly narrowed the gap between
supervised and unsupervised learning on computer vision tasks. In this paper,
we explore their application to geo-located datasets, e.g. remote sensing,
where unlabeled data is often abundant but labeled data is scarce. We first
show that due to their different characteristics, a non-trivial gap persists
between contrastive and supervised learning on standard benchmarks. To close
the gap, we propose novel training methods that exploit the spatio-temporal
structure of remote sensing data. We leverage spatially aligned images over
time to construct temporal positive pairs in contrastive learning and
geo-location to design pre-text tasks. Our experiments show that our proposed
method closes the gap between contrastive and supervised learning on image
classification, object detection and semantic segmentation for remote sensing.
Moreover, we demonstrate that the proposed method can also be applied to
geo-tagged ImageNet images, improving downstream performance on various tasks.
Project Webpage can be found at this link geography-aware-ssl.github.io.
