A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, Using Machine Learning.

Impact cratering is one of the most important processes shaping planetary surfaces, offering valuable clues about the target body's geologic history and composition. However, crater mapping has historically been done manually, a process that has proven to be both arduous and time consuming. This paper outlines a machine learning crater mapping approach for bodies with limited elevation data available (Digital Elevation Models). We applied a Convolutional Neural Network for the detection and morphometry of impact craters on Saturn's moon Enceladus using light‐shadow labels trained on data from the Cassini Imaging Science Subsystem. Our algorithm identified a total of 5,240 features which were used to quantify crater distribution; this included the highest number of small craters (<1–2 km in diameter) recorded on Enceladus by any previous published study. The pool of features was later down‐selected to craters between 0 and 30°N (latitude) imaged at high incidence (>60°) and phase angles (>26°). The down selection was necessary to accurately perform diameter measurements and derive depths from shadow estimation techniques to calculate depth–diameter ratios (d/D); a well‐studied relationship used to constrain planetary surface properties. Results show that the d/D ratio of craters in the equatorial region of Enceladus range from ∼0.06 to 0.37, with a median of 0.19. Our results will inform efforts to constrain the surface properties of this region of Enceladus, potentially also supporting future mission concept design for the Saturnian moon. Future work will explore the simple‐to‐complex crater transition and differences between this area's d/D and Enceladus' northern and southern latitudes. Plain Language Summary: Saturn's moon Enceladus epitomizes the statement "tiny but mighty." A mere ∼500 km in diameter, this body boasts a remarkable array of diverse terrains, a subsurface ocean, and is a strong candidate in the search for extraterrestrial life. As such, it is no surprise that it was named a top research priority in the 2023–2032 Planetary Decadal Survey. The study of impact craters is regarded as one of the most important tools in the study of planetary surfaces, providing insights into the history of celestial bodies. However, traditional manual approaches have proven to be arduous and time intensive. Motivated by the abundance of Cassini mission imagery, the lack of updated studies of Enceladus' cratered terrains and seeking a method to avoid intensive hand‐mapping, we developed a machine learning approach for crater identification and morphometry determination on Enceladus. This approach recorded more smaller craters (<1–2 km in diameter) on Enceladus than any previous published study and supports a history of intense geologic activity and heat flow in the leading and trailing hemispheres. Our method also allowed us to calculate the depth‐diameter relationship for craters in the equatorial region, a relationship that will inform the community's knowledge of the geological characteristics of the region. Key Points: Crater counting and identification provides information about a planet's history and properties, but is time‐consuming when done by handWe used a machine learning approach to map over 5,000 craters on the surface of Enceladus, using highlight‐shadow labelsWe present the depth–diameter ratio for craters on Enceladus' equator to aid future efforts to constrain surface properties of the region [ABSTRACT FROM AUTHOR]