Lunar Impact Crater Identification and Age Estimation with Chang’E
ARTICLE https://doi.org/10.1038/s41467-020-20215-y OPEN Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning ✉ Chen Yang 1,2 , Haishi Zhao 3, Lorenzo Bruzzone4, Jon Atli Benediktsson 5, Yanchun Liang3, Bin Liu 2, ✉ ✉ Xingguo Zeng 2, Renchu Guan 3 , Chunlai Li 2 & Ziyuan Ouyang1,2 1234567890():,; Impact craters, which can be considered the lunar equivalent of fossils, are the most dominant lunar surface features and record the history of the Solar System. We address the problem of automatic crater detection and age estimation. From initially small numbers of recognized craters and dated craters, i.e., 7895 and 1411, respectively, we progressively identify new craters and estimate their ages with Chang’E data and stratigraphic information by transfer learning using deep neural networks. This results in the identification of 109,956 new craters, which is more than a dozen times greater than the initial number of recognized craters. The formation systems of 18,996 newly detected craters larger than 8 km are esti- mated. Here, a new lunar crater database for the mid- and low-latitude regions of the Moon is derived and distributed to the planetary community together with the related data analysis. 1 College of Earth Sciences, Jilin University, 130061 Changchun, China. 2 Key Laboratory of Lunar and Deep Space Exploration, National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China. 3 Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 130012 Changchun, China. 4 Department of Information Engineering and Computer ✉ Science, University of Trento, I-38122 Trento, Italy.
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