Morphological Features-Based Descriptive Index System for Lunar Impact Craters

Lunar impact craters are important for studying lunar surface morphology because they are the most typical morphological units of the Moon. Impact crater descriptive indices can be used to describe morphological features and thus provide direct evidence for both the current state and evolution history of the Moon. Current description methods for lunar impact craters are predominantly qualitative, and mostly focus on their morphological profiles. Less attention is paid to the detailed morphological features inside and outside of the craters. A well-established and descriptive index system is required to describe the real morphological features of lunar impact craters, which are complex in a systematic way, and further improve study, such as heterogeneity analyses of lunar impact craters. This study employs a detailed lunar surface morphological analysis to propose a descriptive index system for lunar impact craters, including indices for the description of individual craters based on their morphological characteristics, spatial structures and basic composition (i.e., crater rim, crater wall, crater floor, central uplift, and ejecta), and indices for crater groups, including spatial distribution and statistical characteristics. Based on the proposed descriptive index system, a description standard for lunar impact craters is designed for categorising and describing these indices in a structured manner. To test their usability and effectiveness, lunar impact craters from different locations are manually detected, and corresponding values for different indices are extracted and organised for a heterogeneity analysis. The results demonstrate that the proposed index system can effectively depict the basic morphological features and spatial characteristics of lunar impact craters

Mapping Planetary Surface Ages at Ultimate Resolutions with Machine Learning: The Moon

The density of impact craters upon a terrestrial surface can give an accurate estimate of the surface's formation age. The Moon has hundreds of millions of impact craters scattered across its surface. Through the power of machine learning, we can automatically count those craters to date any surface on the Moon