The goal of the project is to investigate the relationship between two important paradigms in Machine Learning driven Earth Observation. The first is sensor fusion - combining data from multiple sensors when building ML prediction models. The second one is self-supervised learning, an exciting research direction in deep learning research that enables the usage of large quantities of unlabelled data to construct representation extraction models.