{"id":4620,"date":"2020-12-24T10:24:52","date_gmt":"2020-12-24T09:24:52","guid":{"rendered":"https:\/\/rslab.disi.unitn.it\/?p=4620"},"modified":"2021-10-21T13:38:53","modified_gmt":"2021-10-21T11:38:53","slug":"moon-caters-and-artificial-intelligence-on-nature","status":"publish","type":"post","link":"https:\/\/rslab.disi.unitn.it\/web\/moon-caters-and-artificial-intelligence-on-nature\/","title":{"rendered":"Moon Caters and Artificial Intelligence on Nature"},"content":{"rendered":"<p>A new Moon crater database has been generated by using advanced deep learning and transfer learning methodologies applied to lunar images.\u00a0More than 109,000 previously unrecognized craters have been identified on the Moon\u2019s surface,\u00a0dozens of times larger than the number previously recognized, and\u00a0the ages of 18,996 of these has been estimated. The article has been published on &#8220;Nature Communications&#8221; this week.<\/p>\n<p>RSLab is part of this research through the activity of\u00a0 Prof. Bruzzone. Moreover, the main author, Prof. Yang Chen, spent few years\u00a0 at RSLab before as visiting PhD student and then as Post-doc researcher.<\/p>\n<div id=\"attachment_4623\" style=\"width: 635px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/rslab.disi.unitn.it\/web\/wp-content\/uploads\/2020\/12\/CWCW3JVM5FGDHEW32I2TS7JHIM.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-4623\" class=\"size-large wp-image-4623\" src=\"https:\/\/rslab.disi.unitn.it\/web\/wp-content\/uploads\/2020\/12\/CWCW3JVM5FGDHEW32I2TS7JHIM-1024x768.jpg\" alt=\"\" width=\"625\" height=\"469\" srcset=\"https:\/\/rslab.disi.unitn.it\/web\/wp-content\/uploads\/2020\/12\/CWCW3JVM5FGDHEW32I2TS7JHIM-1024x768.jpg 1024w, https:\/\/rslab.disi.unitn.it\/web\/wp-content\/uploads\/2020\/12\/CWCW3JVM5FGDHEW32I2TS7JHIM-300x225.jpg 300w, https:\/\/rslab.disi.unitn.it\/web\/wp-content\/uploads\/2020\/12\/CWCW3JVM5FGDHEW32I2TS7JHIM-150x112.jpg 150w, https:\/\/rslab.disi.unitn.it\/web\/wp-content\/uploads\/2020\/12\/CWCW3JVM5FGDHEW32I2TS7JHIM-768x576.jpg 768w, https:\/\/rslab.disi.unitn.it\/web\/wp-content\/uploads\/2020\/12\/CWCW3JVM5FGDHEW32I2TS7JHIM-1536x1151.jpg 1536w, https:\/\/rslab.disi.unitn.it\/web\/wp-content\/uploads\/2020\/12\/CWCW3JVM5FGDHEW32I2TS7JHIM-2048x1535.jpg 2048w, https:\/\/rslab.disi.unitn.it\/web\/wp-content\/uploads\/2020\/12\/CWCW3JVM5FGDHEW32I2TS7JHIM-170x127.jpg 170w\" sizes=\"auto, (max-width: 625px) 100vw, 625px\" \/><\/a><p id=\"caption-attachment-4623\" class=\"wp-caption-text\">NASA\/GSFC\/Arizona State University<\/p><\/div>\n<p><em>Nature Press Release<\/em><strong><br \/>\n<\/strong><\/p>\n<blockquote><p><strong>Planetary science:\u00a0 Over 100,000 new craters identified on the Moon<\/strong><\/p>\n<p>More than 109,000 previously unrecognized craters have been identified on the Moon\u2019s surface, reports a study published in\u00a0<em>Nature Communications<\/em>\u00a0this week.<\/p>\n<p>Craters occupy most of the surface of the Moon. However, manual and automatic methods to detect the number of craters have resulted in inconsistencies as to the precise total. For example, it is often hard to detect irregular or degraded craters using automatic methods.<\/p>\n<p>Chen Yang and colleagues set out to identify lunar impact craters using a transfer learning strategy \u2014 a machine learning approach in which previous knowledge gained is used to solve a further problem. The authors first trained a deep neural network using data from 7,895 previously identified and 1,411 dated craters. Using data from the Chang\u2019E-1 and Chang\u2019E-2 orbiters, the network was able to identify 109,956 new craters \u2014 dozens of times larger than the number previously recognized throughout the mid- and low-latitude regions of the Moon \u2014 including 46 with diameters ranging from 200 to 550 kilometres. Of the craters with a diameter larger than 8 kilometres, the network estimated the ages of 18,996 of these. The findings have resulted in the creation of a new lunar crater database of the mid- and low-latitude regions of the Moon.<\/p>\n<p>The authors suggest that their approach could be adapted for use with other bodies in the Solar System and could help extract more information than is possible with manual analysis methods.<\/p><\/blockquote>\n<p>Read the full article on nature communications: <a href=\"https:\/\/doi.org\/10.1038\/s41467-020-20215-y\">https:\/\/www.nature.com\/articles\/s41467-020-20215-y<\/a><\/p>\n<p>Read the interview on POPULAR SCIENCE: <a href=\"https:\/\/www.popsci.com\/story\/science\/bot-counted-new-moon-craters\/\">https:\/\/www.popsci.com\/story\/science\/bot-counted-new-moon-craters\/<\/a><\/p>\n<p>About this:<\/p>\n","protected":false},"excerpt":{"rendered":"<p>RSLab is part of a research that generated a new Moon crater database by using advanced deep learning and transfer learning methodologies applied to lunar images.\u00a0\u00a0More than 109,000 previously unrecognized craters have been identified on the Moon\u2019s surface.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[6,8],"tags":[51,53,50,18],"class_list":["post-4620","post","type-post","status-publish","format-standard","hentry","category-news","category-press","tag-ai","tag-deep-neural-network","tag-moon","tag-space"],"_links":{"self":[{"href":"https:\/\/rslab.disi.unitn.it\/web\/wp-json\/wp\/v2\/posts\/4620","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rslab.disi.unitn.it\/web\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rslab.disi.unitn.it\/web\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rslab.disi.unitn.it\/web\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/rslab.disi.unitn.it\/web\/wp-json\/wp\/v2\/comments?post=4620"}],"version-history":[{"count":10,"href":"https:\/\/rslab.disi.unitn.it\/web\/wp-json\/wp\/v2\/posts\/4620\/revisions"}],"predecessor-version":[{"id":4632,"href":"https:\/\/rslab.disi.unitn.it\/web\/wp-json\/wp\/v2\/posts\/4620\/revisions\/4632"}],"wp:attachment":[{"href":"https:\/\/rslab.disi.unitn.it\/web\/wp-json\/wp\/v2\/media?parent=4620"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rslab.disi.unitn.it\/web\/wp-json\/wp\/v2\/categories?post=4620"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rslab.disi.unitn.it\/web\/wp-json\/wp\/v2\/tags?post=4620"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}