Precision Lunar Landscape Unveiled with Terrain Mapping and Advanced Techniques

The study introduces an innovative lunar exploration method employing deep neural networks, specifically Long Short-Term Memory (LSTM), to enhance Terrain Mapping Camera (TMC) data resolution to an unprecedented 5-meter precision. The primary objective is to surpass the capabilities of the Orthogonal High-Resolution Camera (OHRC). The proposed approach will undergo rigorous validation through k-fold cross-validation, aiming to provide unparalleled insights into lunar terrain. Leveraging the refined data, the study aims to create an advanced hazard map, promising a revolutionary impact on mission planning and ensuring safer lunar navigation. Going beyond, the methodology extends to real-time navigation techniques using the enhanced TMC dataset. By integrating high-resolution techniques, hazard mapping, real-time navigation, and robust model validation, this approach has the potential to establish a new benchmark for lunar missions. The anticipated outcomes may significantly advance our understanding of lunar landscapes, enhancing safety protocols and navigation strategies for future lunar exploration endeavors.