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

Space Sciences

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

C. Yang, H. Zhao, et al.

This research reveals groundbreaking advancements in lunar impact crater detection and age estimation utilizing Chang'E data. The team significantly expands the crater database with over 109,000 new identifications and age estimates for nearly 19,000 craters, making this valuable information publicly available for further exploration.

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Abstract
This paper addresses the automatic detection and age estimation of lunar impact craters using Chang'E data and stratigraphic information. Through transfer learning with deep neural networks, the researchers significantly expanded the existing crater database, identifying 109,956 new craters (more than a dozen times the initial number) and estimating the formation ages of 18,996 craters larger than 8 km. This new lunar crater database, covering mid- and low-latitude regions, is publicly available.
Publisher
Nature Communications
Published On
Dec 22, 2020
Authors
Chen Yang, Haishi Zhao, Lorenzo Bruzzone, Jon Atli Benediktsson, Yanchun Liang, Bin Liu, Xingguo Zeng, Renchu Guan, Chunlai Li, Ziyuan Ouyang
Tags
lunar impact craters
Chang'E data
transfer learning
deep neural networks
crater database
age estimation

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