AI identifies sites on Moon for landing and exploration

The Moon-scanning method from an international team automatically classifies important lunar features from telescope images.

The choice of future landing and exploration sites may come down to the most promising locations for construction, minerals or energy resources but scanning by eye across such a large area is laborious and often inaccurate.

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Siyuan Chen, Xin Gao and Shuyu Sun at KAUST in Saudi Arabia, along with colleagues from The Chinese University of Hong Kong, have now applied machine learning and 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 said in a statement. “Both have been detected in Moon craters and could be useful resources for replenishing spacecraft fuel.”