  The application of deep neural networks to remote sensing imagery is often
constrained by the lack of ground-truth annotations. Adressing this issue
requires models that generalize efficiently from limited amounts of labeled
data, allowing us to tackle a wider range of Earth observation tasks. Another
challenge in this domain is developing algorithms that operate at variable
spatial resolutions, e.g., for the problem of classifying land use at different
scales. Recently, self-supervised learning has been applied in the remote
sensing domain to exploit readily-available unlabeled data, and was shown to
reduce or even close the gap with supervised learning. In this paper, we study
self-supervised visual representation learning through the lens of label
efficiency, for the task of land use classification on
multi-resolution/multi-scale satellite images. We benchmark two contrastive
self-supervised methods adapted from Momentum Contrast (MoCo) and provide
evidence that these methods can be perform effectively given little downstream
supervision, where randomly initialized networks fail to generalize. Moreover,
they outperform out-of-domain pretraining alternatives. We use the large-scale
fMoW dataset to pretrain and evaluate the networks, and validate our
observations with transfer to the RESISC45 dataset.
