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  Peering into lunar permanently shadowed regions with deep learning

Bickel, V. T., Moseley, B., Lopez-Francos, I., & Shirley, M. (2021). Peering into lunar permanently shadowed regions with deep learning. Nature Communications, 12: 5607. doi:10.1038/s41467-021-25882-z.

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Bickel, Valentin Tertius1, Autor           
Moseley, B., Autor
Lopez-Francos, I., Autor
Shirley, M., Autor
Affiliations:
1Department Planets and Comets, Max Planck Institute for Solar System Research, Max Planck Society, ou_1832288              

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 Zusammenfassung: 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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 Datum: 2021
 Publikationsstatus: Online veröffentlicht
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 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1038/s41467-021-25882-z
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Titel: Nature Communications
  Kurztitel : Nat. Commun.
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: London : Nature Publishing Group
Seiten: - Band / Heft: 12 Artikelnummer: 5607 Start- / Endseite: - Identifikator: ISSN: 2041-1723
CoNE: https://pure.mpg.de/cone/journals/resource/2041-1723