Peering into lunar permanently shadowed regions with deep learning

V. T. Bickel (), B. Moseley, I. Lopez-Francos and M. Shirley
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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

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-25882-z

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