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Peering into lunar permanently shadowed regions with deep learning

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...

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Autores principales: Bickel, V. T., Moseley, B., Lopez-Francos, I., Shirley, M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460740/
https://www.ncbi.nlm.nih.gov/pubmed/34556656
http://dx.doi.org/10.1038/s41467-021-25882-z
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author Bickel, V. T.
Moseley, B.
Lopez-Francos, I.
Shirley, M.
author_facet Bickel, V. T.
Moseley, B.
Lopez-Francos, I.
Shirley, M.
author_sort Bickel, V. T.
collection PubMed
description 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.
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spelling pubmed-84607402021-10-22 Peering into lunar permanently shadowed regions with deep learning Bickel, V. T. Moseley, B. Lopez-Francos, I. Shirley, M. Nat Commun Article 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. Nature Publishing Group UK 2021-09-23 /pmc/articles/PMC8460740/ /pubmed/34556656 http://dx.doi.org/10.1038/s41467-021-25882-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Bickel, V. T.
Moseley, B.
Lopez-Francos, I.
Shirley, M.
Peering into lunar permanently shadowed regions with deep learning
title Peering into lunar permanently shadowed regions with deep learning
title_full Peering into lunar permanently shadowed regions with deep learning
title_fullStr Peering into lunar permanently shadowed regions with deep learning
title_full_unstemmed Peering into lunar permanently shadowed regions with deep learning
title_short Peering into lunar permanently shadowed regions with deep learning
title_sort peering into lunar permanently shadowed regions with deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460740/
https://www.ncbi.nlm.nih.gov/pubmed/34556656
http://dx.doi.org/10.1038/s41467-021-25882-z
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