Classifying Safe Lunar Landing Sites Using Deep Learning and LRO Data

In this study, we developed and implemented a deep learning program to classify the suitability of regions for landing on the Moon's south pole, utilizing data from NASA's Lunar Reconnaissance Orbiter (LRO), launched in 2009. The LRO mission, which included the creation of a three-dimensional map of the Moon using the Lunar Orbiter Laser Altimeter (LOLA) instrument, and high-resolution images from the Lunar Reconnaissance Orbiter Camera (LROC), enabled detailed mapping of lunar topography, with a particular focus on craters in the south polar region. Images with a resolution of 1 - 20 meters per pixel were analyzed and characterized using a Convolutional Neural Network (CNN) to identify terrain features that could indicate safe landing sites. The binary analysis classified the areas as either suitable or unsuitable for landing, considering risks such as explosion or overturning due to the terrain. This approach enhances the identification of secure landing sites, supporting future lunar missions like Artemis 3.