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Automated or semiautomated tools for Martian data analysis can substantially broaden the scope of scientific inquiry. Recognizing this opportunity, we've undertaken research to apply pattern-recognition and machine-learning tools to automatic analysis and characterization of the Mars surface. This research includes machine surveys of specific Iandforms, such as impact craters and valley networks, and automatic generation of geomorphic maps. A geomorphic map is a thematic map of topographical expressions or landforms. Machine learning can play a vital role in automating this mapping process. A learning system can employ clustering techniques to fully automate the discovery of meaningful landform classes. A clustering tool based on unsupervised learning offers maximum automation for geomorphic mapping.