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.”
Machine learning is a very effective technique for training an AI model to look for certain features on its own. The first problem faced by Chen and his colleagues was that there was no labelled dataset for rilles that could be used to train their model.