The choice of future landing and exploration sites on the Moon may come down to the most promising prospective locations for construction, minerals, or potential energy resources. But scanning across a large area, looking for features a few hundred metres across, by eye, is laborious and often inaccurate, experts have said, which makes it difficult to pick optimal areas for exploration.
Siyuan Chen, Xin Gao, and Shuyu Sun at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, along with colleagues from the Chinese University of Hong Kong, have now applied machine learning and artificial intelligence (AI) to automate the identification of prospective lunar landing and exploration areas.
“We are looking for lunar features like craters and rilles, which are thought to be hotspots for energy resources like uranium and helium-3 – a promising resource for nuclear fusion,” Chen explained. “Both have been detected in Moon craters and...