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In this paper, we present a methodology for automatic geomorphic mapping of planetary surfaces that incorporates machine-learning techniques. Our application transforms remotely sensed topographic data gathered by orbiting satellites into semantically meaningful maps of landforms; such maps are valuable research tools for planetary science. As topographic data become increasingly available, the ability to derive geomorphic maps efficiently is becoming essential. In our proposed framework, the mapping is achieved by means of scene segmentation followed by supervised classification of segments. The two mapping steps use different sets of features derived from digital elevation models of planetary surfaces; selection of appropriate features is discussed. Using a particular set of terrain attributes relevant to annotating cratered terrain on Mars, we investigate the design choices for both segmentation and classification components. The segmentation assessment includes K-means-based agglomerative segmentation and watershed-based segmentation. The classification assessment includes three supervised learning algorithms: Naive Bayes, Bagging with decision trees, and support-vector machines (SVMs); segments are classified into the following landforms: crater floors, crater walls (concave and convex), ridges (concave and convex), and intercrater plains. The method is applied to six test sites on Mars. The analysis of the results shows that a combination of K -means-based agglomerative segmentation and either SVM with a quadratic kernel or Bagging with C4.5 yields best maps. The presented framework can be adopted to generate geomorphic maps of sites on Earth.