Scientists Siyuan Chen, Xin Gao and Shuyu Sun as well as researchers from The Chinese University of Hong Kong, are using applied machine learning and artificial intelligence to automate the identification of potential landing as well as exploration sites on the Moon. Their goal is to look for lunar features such as craters and rilles, which are allegedly hot spots for energy resources like uranium and helium-3—a for nuclear fusion.
Both uranium and helium-3—a have been detected in moon craters and could be very useful resources for replenishing spacecraft fuel. One issue they encountered was that there was no labeled dataset for rilles, so the scientists had to construct their own training dataset with annotations for both craters as well as rilles. They used an approach called transfer learning to pre-train their rille model on a surface crack dataset with some fine tuning using actual rille masks. Next, they developed a computational approach to identify craters and rilles simultaneously.
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This is a pixel-to-pixel problem for which we need to accurately mask the craters and rilles in a lunar image. We solved this problem by constructing a deep learning framework called high-resolution-moon-net, which has two independent networks that share the same network architecture to identify craters and rilles simultaneously,” said Siyuan Chen.
