On the Mars surface, various geological formations can be seen, among them are mounds. Mounds and cones can be formed through mud extrusion and periglacial processes such as freezing lenses of water (pingo). Their location helps pin down when water on the Red Planet dried up during a global climate change event. The mapping of large populations of mounds with ML algorithms is important for future Mars exploration. With the ML pipeline developed for this science case, the goal is to identify the mound populations, providing an automatic mapping and classification tool. Based on their topographic/morphometric parameters we aim at obtaining further classifications. The ultimate goal would be to have the precision score for such classification of 90 percent or higher.

The data is obtained from the Mars Reconaissance Orbiter (MRO) mission. The MRO spacecraft is designed to study the geology and climate of Mars, provide reconnaissance of future landing sites, and relay data from surface missions back to Earth. The data was collected by the High Resolution Imaging Science Experiment, also known as HIRISE. HiRISE is the most powerful camera ever sent to another planet, one of six instruments on board the MRO. The data is in the format of Digital Elevation Model (DEM). Detailed description of the data can be found on the University of Arizona HiRISE website.