  Remote sensing and automatic earth monitoring are key to solve global-scale
challenges such as disaster prevention, land use monitoring, or tackling
climate change. Although there exist vast amounts of remote sensing data, most
of it remains unlabeled and thus inaccessible for supervised learning
algorithms. Transfer learning approaches can reduce the data requirements of
deep learning algorithms. However, most of these methods are pre-trained on
ImageNet and their generalization to remote sensing imagery is not guaranteed
due to the domain gap. In this work, we propose Seasonal Contrast (SeCo), an
effective pipeline to leverage unlabeled data for in-domain pre-training of
re-mote sensing representations. The SeCo pipeline is com-posed of two parts.
First, a principled procedure to gather large-scale, unlabeled and uncurated
remote sensing datasets containing images from multiple Earth locations at
different timestamps. Second, a self-supervised algorithm that takes advantage
of time and position invariance to learn transferable representations for
re-mote sensing applications. We empirically show that models trained with SeCo
achieve better performance than their ImageNet pre-trained counterparts and
state-of-the-art self-supervised learning methods on multiple downstream tasks.
The datasets and models in SeCo will be made public to facilitate transfer
learning and enable rapid progress in re-mote sensing applications.
