Underground Habitation on Mars: A Comprehensive Review of Terrain Analysis and Machine Learning Applications

Abstract

As humanity advances towards the exploration and potential colonization of Mars, the harsh surface conditions necessitate innovative solutions for sustainable habitation. This review examines the concept of underground habitation on Mars, focusing on the challenges posed by the Martian environment, the diverse terrain types, and the application of machine learning in analyzing vast datasets for optimal site selection. We discuss the findings from past Mars missions, the composition of Martian soil, and the criteria for selecting landing sites. Furthermore, we explore the role of machine learning in processing and interpreting large volumes of data from various sources, including satellite imagery and rover observations. This review aims to provide a comprehensive understanding of the current state of Mars exploration and the potential for underground habitation, highlighting the intersection of planetary science, geology, and artificial intelligence in advancing our capabilities for future Mars missions.