An international team appears to be scanning the Moon with an AI system and locating areas for landing and exploration.
The choice of future landing and exploration sites on the Moon may lead to the most promising sites for construction, minerals or energy resources, but visual detection in such a large area is arduous and often inaccurate.
Siyuan Chen, Xin Gao and Shuyu Sun from KAUST (King Abdullah University of Science and Technology) in Saudi Arabia, along with colleagues from the Chinese University of Hong Kong, have now implemented machine learning and artificial intelligence (AI) to automate the identification of possible landing and exploration areas on the Moon.
Machine learning is a very effective technique for training an AI model to look for specific features on its own. The first problem Siyuan Chen and his colleagues faced was that there was no set of data tags for rilles that could be used to train their model.
According to KAUST, the next challenge was to develop a computational approach that could be used to simultaneously locate craters and rilles, which has never been done before.
The group approach is said to have achieved an accuracy of up to 83,7% which is higher than existing crater detection methods. Their findings have been published in Applied Energy.
Source of information: theengineer.co.uk