Deep Learning-Driven Detection and Mapping of Rockfalls on Mars

This project introduces a novel approach to the detection and mapping of rockfalls on Mars through the application of deep learning techniques. Leveraging advanced neural networks, the system is trained on Martian landscape imagery to autonomously identify and categorize rockfall events. The deep learning model not only detects rockfalls but also maps their locations, providing valuable insights into the dynamic geological processes on Mars. This application of deep learning to planetary exploration contributes to our understanding of Martian surface dynamics and hazards. The project aligns with the broader goal of enhancing the efficiency of autonomous space exploration by leveraging artificial intelligence for real-time analysis of extraterrestrial landscapes.

Keywords:

Deep Learning, Rockfall Detection, nasnet, mobilenet, cnn, Mapping, Martian Surface, Planetary Exploration, Neural Networks, Autonomous Space Exploration, Geologic Processes, Mars, Image Analysis.