Geography-Aware Self-Supervised Learning. (arXiv:2011.09980v7 [cs.CV] UPDATED)

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. …

arxiv cv geography learning self-supervised learning supervised learning