Training current deep neural network architectures for automated earth remote sensing raises the necessity of huge amounts of labeled data or the application of suitable transfer learning approaches. Unfortunately, transfer learning representations learned on ImageNet are not well suited for the remote sensing domain. Fortunately, self-supervised learning (SSL) is stepping in to fill this gap, by providing domain specific representations without the need for labels. Moreover, in contrast to natural scenes, earth’s land cover classes change with seasons, which has to be considered during training and data collection. Luckily, unlabeled observations over different seasons at the same location are easy to come by, with a lot of publicly available data from, e.g., the Sentinel-2 mission and automated sampling approaches.

Research on the use of augmentations in physically constrained remote sensing scenarios, like the analysis of Martian surface data, is largely unexplored. In this work we present an analysis on how reasonable augmentation strategies can be selected which are class agnostic and respect physical plausibility in supervised and weakly-supervised tasks. Additionally, we present the first results of self-supervised learning on Martian surface data, discuss the importance of physically plausible augmentations in the context of self-supervised learning, specifically contrastive learning, and provide a comprehensive overview of the generalization properties induced by different augmentation strategies with the help of geomorphic maps.