The large volume of high-resolution images acquired by the Mars Reconnaissance Orbiter has opened a new frontier for developing automated approaches to detecting landforms on the surface of Mars. However, most landform classifiers focus on crater detection, which represents only one of many geological landforms of scientific interest. In this work, we use Convolutional Neural Networks (ConvNets) to detect both volcanic rootless cones and transverse aeolian ridges. Our system, named MarsNet, consists of five networks, each of which is trained to detect landforms of different sizes. We compare our detection algorithm with a widely used method for image recognition, Support Vector Machines (SVMs) using Histogram of Oriented Gradients (HOG) features. We show that ConvNets can detect a wide range of landforms and has better accuracy and recall in testing data than traditional classifiers based on SVMs.

The Medusae Fossae Formation (MFF) on Mars is an intensely eroded deposit north of the cratered highlands. It is widely thought that MFF materials were emplaced through ignimbrite eruptions. Recent geologic mapping of western MFF identified outliers of MFF materials well beyond the previously mapped western extent for the deposit, including outliers close to Gale crater. We report counts of impact craters on the MFF units that have implications for our understanding of the general history of MFF and the uppermost layered materials on the Gale crater mound.

Determinations of soil moisture and sediment availability in arid regions are important indicators of local climate variability and the potential for future dust storm events. Data from the Advanced Spaceborne Thermal Emission and Reflection (ASTER) radiometer were used to derive the relationships among potential soil erosion, soil moisture, and thermal inertia (TI) at the spatial scale of aeolian landforms for the White Sands Dune Field between May 2000 and March 2008. Land surface apparent thermal inertia (ATI) data were used to derive an approximation of actual TI in order to estimate the wind threshold velocity ratio (WTR). The WTR is a ratio of the wind velocity thresholds at which soil erosion occurs for wet soil versus dry soil. The ASTER‐derived soil moisture retrievals and the changes through time at White Sands were interpreted to be driven primarily by precipitation, but the presence of a perched groundwater table may also influence certain areas. The sediment availability of dunes, active playa surfaces and the margin of the alluvial fans to the west were determined to be consistently higher than the surrounding area. The sediment availability can be primarily explained by precipitation events and the number of dry days prior to the data acquisition. Other factors such as vegetation and the amount of surface crusting may also influence soil mobility, but these were not measured in the field. This approach showed the highest modeled sediment availability values just days prior to the largest dust emission event at White Sands in decades. Such an approach could be extended to a global monitoring technique for arid land systems that are prone to dust storms and for other regional land surface studies in the Sahara.