Multi-modal Self-supervised Learning for Earth Observation

An ever-increasing number of Earth Observation satellites continually captures massive amounts of remote sensing data. This wealth of data makes manual analysis of all images by human experts impossible. At the same time, the data lacks readily available labels which are necessary for training supervised machine learning models, including state-of-the art deep learning approaches. Techniques from self-supervised machine learning make it possible to leverage unlabeled data for the training of deep neural network models, and thus improve our Earth Observation capabilities across different tasks.