  Collecting large annotated datasets in Remote Sensing is often expensive and
thus can become a major obstacle for training advanced machine learning models.
Common techniques of addressing this issue, based on the underlying idea of
pre-training the Deep Neural Networks (DNN) on freely available large datasets,
cannot be used for Remote Sensing due to the unavailability of such large-scale
labeled datasets and the heterogeneity of data sources caused by the varying
spatial and spectral resolution of different sensors. Self-supervised learning
is an alternative approach that learns feature representation from unlabeled
images without using any human annotations. In this paper, we introduce a new
method for land cover mapping by using a clustering based pretext task for
self-supervised learning. We demonstrate the effectiveness of the method on two
societally relevant applications from the aspect of segmentation performance,
discriminative feature representation learning and the underlying cluster
structure. We also show the effectiveness of the active sampling using the
clusters obtained from our method in improving the mapping accuracy given a
limited budget of annotating.
