  Autonomous terrain classification is an important problem in planetary
navigation, whether the goal is to identify scientific sites of interest or to
traverse treacherous areas safely. Past Martian rovers have relied on human
operators to manually identify a navigable path from transmitted imagery. Our
goals on Mars in the next few decades will eventually require rovers that can
autonomously move farther, faster, and through more dangerous
landscapes--demonstrating a need for improved terrain classification for
traversability. Autonomous navigation through extreme environments will enable
the search for water on the Moon and Mars as well as preparations for human
habitats. Advancements in machine learning techniques have demonstrated
potential to improve terrain classification capabilities for ground vehicles on
Earth. However, classification results for space applications are limited by
the availability of training data suitable for supervised learning methods.
This paper contributes an open source automatic data processing pipeline that
uses camera geometry to co-locate Curiosity and Perseverance Mastcam image
products with Mars overhead maps via ray projection over a terrain model. In
future work, this automated data processing pipeline will be leveraged for
development of machine learning methods for terrain classification.
