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Data analysis methods have scarcely kept pace with the rapid increase in Earth observations, spurring the development of novel algorithms, storage methods, and computational techniques. For scientists interested in Mars, the problem is always the same: there is simultaneously never enough of the right data and an overwhelming amount of data in total. Finding sufficient data needles in a haystack to test a hypothesis requires hours of manual data screening, and more needles and hay are added constantly. To date, the vast majority of Martian research has been focused on either one-off local/regional studies or on hugely time-consuming manual global studies. Machine learning in its numerous forms can be helpful for future such work. Machine learning has the potential to help map and classify a large variety of both features and properties on the surface of Mars and to aid in the planning and execution of future missions. Here, we outline the current extent of machine learning as applied to Mars, summarize why machine learning should be an important tool for planetary geomorphology in particular, and suggest numerous research avenues and funding priorities for future efforts. We conclude that: (1) moving toward methods that require less human input (i.e., self- or semi-supervised) is an important paradigm shift for Martian applications, (2) new robust methods using generative adversarial networks to generate synthetic high-resolution digital terrain models represent an exciting new avenue for Martian geomorphologists, (3) more effort and money must be directed toward developing standardized datasets and benchmark tests, and (4) the community needs a large-scale, generalized, and programmatically accessible geographic information system (GIS). [ABSTRACT FROM AUTHOR]

New approaches for comparative planetology are vital to recognize and assess possible shared formative processes that shape landforms on different planetary bodies, and in the case of this study to identify possible cirques preserved from past martian climates that may have been more favorable to equatorial ice. We used "RetinaNet", an augmented Convolutional Neural Network (CNN) initially trained with digital elevation data from over 1950 terrestrial cirques, to identify similar forms along a 50x50km area of the equatorial martian Dichotomy Boundary east of Gale Crater. A significant number of false positive detections associated with CTX stereo pair derived DEM artifacts and tile edges as well as craters were automatically removed. High confidence levels assigned by the machine learning algorithm suggest that the 16-remaining cirque-like forms may have formed and evolved under similar conditions to their terrestrial analogs. Morphometric data extracted for each of these cirque-like forms was statistically compared to terrestrial analog morphometric measures. While cirque length and width were significantly larger than their terrestrial counterparts and the area of these forms was ~150 % larger, on average headwall heights were ~25 % shorter. Allometric relationships suggest that the shape of these martian features changes proportionally as they grow in contrast to terrestrial forms where length increases faster than width and height. This could be due to the weaker gravitational force on Mars (3.721 m/s2) and other effects related to martian climate and environmental conditions, such as cold based glaciation. The identification of cirques in an equatorial region has implications for cryosphere distribution/redistribution and martian geologic and climatic history. • Cirques are bowl-shaped depressions on side of mountains created by glacial flow. • Machine Learning algorithm that detects Cirques trained on Earth was used on Mars. • Cirques on Mars likely to have formed under similar conditions as Earth cirques. • Detected cirque on Mars tropical regions likely associated with climate change. • Martian cryosphere expanded towards tropics during high obliquity in recent past. [ABSTRACT FROM AUTHOR]

Linear dunes and human fingerprints share many characteristics. Both have ridges, valleys, and defects (minutiae) in the form of bifurcations and termination of ridgeline features. For dunes, determining how defects vary across linear and transverse dunefields is critical to understanding the physics of their formative processes and the physical forcing mechanisms that produce dunefields. Unfortunately, manual extraction of defect locations and higher order characteristics (type, orientation, and quality) from remotely sensed imagery is both time-consuming and inconsistent. This problem is further exacerbated when, in the case of imagery from sensors in orbit around Mars, we are unable to field check interpretations. In this research, we apply a novel technique for extracting defects from multiple imagery sources utilizing a robust and well-documented fingerprint minutiae detection and extraction software (MINDTCT: MINutiae DecTeCTion) developed by the National Institute of Standards and Technology (NIST). We apply our 'fingerprinting' approach to Transverse Aeolian Ridges (TARs), relict aeolian features commonly seen on the surface of Mars, whose depositional and formative processes are poorly understood. Our algorithmic approach demonstrates that automating the rapid extraction of defects from orbitally-derived high-resolution imagery of Mars is feasible and produces maps that allow the quantification and analysis of these features. [ABSTRACT FROM AUTHOR]