Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning

Using Chang’E data, the authors here identify more than 109,000 previously unrecognized lunar craters and date almost 19,000 craters based on transfer learning with deep neural networks. A new lunar crater database is derived and distributed to the planetary community.

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Autores principales:	Chen Yang, Haishi Zhao, Lorenzo Bruzzone, Jon Atli Benediktsson, Yanchun Liang, Bin Liu, Xingguo Zeng, Renchu Guan, Chunlai Li, Ziyuan Ouyang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/65bb490c6daa4866aab6382029e35f68
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spelling	oai:doaj.org-article:65bb490c6daa4866aab6382029e35f682021-12-02T13:57:56ZLunar impact crater identification and age estimation with Chang’E data by deep and transfer learning10.1038/s41467-020-20215-y2041-1723https://doaj.org/article/65bb490c6daa4866aab6382029e35f682020-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-20215-yhttps://doaj.org/toc/2041-1723Using Chang’E data, the authors here identify more than 109,000 previously unrecognized lunar craters and date almost 19,000 craters based on transfer learning with deep neural networks. A new lunar crater database is derived and distributed to the planetary community.Chen YangHaishi ZhaoLorenzo BruzzoneJon Atli BenediktssonYanchun LiangBin LiuXingguo ZengRenchu GuanChunlai LiZiyuan OuyangNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-15 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Chen Yang Haishi Zhao Lorenzo Bruzzone Jon Atli Benediktsson Yanchun Liang Bin Liu Xingguo Zeng Renchu Guan Chunlai Li Ziyuan Ouyang Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning
description	Using Chang’E data, the authors here identify more than 109,000 previously unrecognized lunar craters and date almost 19,000 craters based on transfer learning with deep neural networks. A new lunar crater database is derived and distributed to the planetary community.
format	article
author	Chen Yang Haishi Zhao Lorenzo Bruzzone Jon Atli Benediktsson Yanchun Liang Bin Liu Xingguo Zeng Renchu Guan Chunlai Li Ziyuan Ouyang
author_facet	Chen Yang Haishi Zhao Lorenzo Bruzzone Jon Atli Benediktsson Yanchun Liang Bin Liu Xingguo Zeng Renchu Guan Chunlai Li Ziyuan Ouyang
author_sort	Chen Yang
title	Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning
title_short	Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning
title_full	Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning
title_fullStr	Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning
title_full_unstemmed	Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning
title_sort	lunar impact crater identification and age estimation with chang’e data by deep and transfer learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/65bb490c6daa4866aab6382029e35f68
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Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning

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