Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images

  title={Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images},
  author={Tejas Panambur and Deep Chakraborty and Melissa Meyer and Ralph E. Milliken and Erik G. Learned-Miller and Mario Parente},
  journal={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
Automatic terrain recognition in Mars rover images is an important problem not just for navigation, but for scientists interested in studying rock types, and by extension, conditions of the ancient Martian paleoclimate and habit-ability. Existing approaches to label Martian terrain either involve the use of non-expert annotators producing taxonomies of limited granularity (e.g. soil, sand, bedrock, float rock, etc.), or rely on generic class discovery approaches that tend to produce perceptual… 

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