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Peering into lunar permanently shadowed regions with deep learning

Author

Listed:
  • V. T. Bickel

    (Max Planck Institute for Solar System Research)

  • B. Moseley

    (University of Oxford)

  • I. Lopez-Francos

    (NASA Ames Research Center)

  • M. Shirley

    (NASA Ames Research Center)

Abstract

The lunar permanently shadowed regions (PSRs) are expected to host large quantities of water-ice, which are key for sustainable exploration of the Moon and beyond. In the near future, NASA and other entities plan to send rovers and humans to characterize water-ice within PSRs. However, there exists only limited information about the small-scale geomorphology and distribution of ice within PSRs because the orbital imagery captured to date lacks sufficient resolution and/or signal. In this paper, we develop and validate a new method of post-processing LRO NAC images of PSRs. We show that our method is able to reveal previously unseen geomorphological features such as boulders and craters down to 3 meters in size, whilst not finding evidence for surface frost or near-surface ice. Our post-processed images significantly facilitate the exploration of PSRs by reducing the uncertainty of target selection and traverse/mission planning.

Suggested Citation

  • V. T. Bickel & B. Moseley & I. Lopez-Francos & M. Shirley, 2021. "Peering into lunar permanently shadowed regions with deep learning," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25882-z
    DOI: 10.1038/s41467-021-25882-z
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