Machine Learning on Mars: Open Challenges, Similarities and Differences to Earth Remote Sensing

In this work we aim to bridge remote sensing scene understanding and mapping on Earth and Mars. Accordingly, we present common challenges to both domains, like an abundance of data, class imbalances, and the scarcity of high resolution ground-truth data. Additionally, we introduce a novel multi-modal semantic segmentation dataset which is based on a geologic map of a rover landing site on Mars and distill the aforementioned challenges into a single dataset. We present initial results and discuss how well a deep neural network can learn to create geologic maps from orbital images of Mars.