Martian Terrain Classification through Federated Learning: A Decentralized Approach for Understanding the Mars

The exploration of the Martian landmass holds paramount importance in advancing spatial research as it offers valuable insights into planetary evolution and the potential for habitability beyond Earth. In this paper, a novel approach is presented to perform multi-class classification of Martian terrain into seven distinct classes, namely crater, dark dune, slope streak, bright dune, impact ejecta, swiss cheese and spider. Accurate terrain classification provides crucial insights into Martian ge-ological processes and enables informed decisions for selecting optimal landing sites for future missions. Additionally, by facili-tating the planning of safe and efficient traverses during explo-ration missions, precise terrain classification enhances the overall success and safety of these ambitious endeavors, ultimately advancing our knowledge of Mars and supporting humanity's quest for space exploration. This research harnesses the power of federated learning, an emerging decentralized machine learning paradigm, in conjunction with the DenseNet-121 architecture. This collaborative learning technique enables the model training across distributed data sources while preserving data privacy and security. Through extensive experimentation using the HiRISE dataset, obtained from the Mars Reconnaissance Orbiter, this research demonstrates the efficacy of the presented approach in achieving better performance and robustness. The federated DenseNet-121 model showcases promising results, laying the foundation for efficient multi-class Martian terrain classification in future spatial exploration missions and contributing to a deeper understanding of the Martian landscape.