Seasonal Contrast: Unsupervised Pre-Training From Uncurated Remote Sensing Data

Oscar Mañas, Alexandre Lacoste, Xavier Giró-i-Nieto, David Vazquez, Pau Rodriguez

Abstract:

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 remote sensing representations. The SeCo pipeline is composed 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 remote 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 remote sensing applications.