Machine Learning Driven Detection of 1 Billion+ Lunar Impact Craters In Permanently Shadowed Regions Using ShadowCam Data

Until recently, permanently shadowed regions (PSRs) on the Moon were devoid of high-resolution high-signal-to-noise imaging. ShadowCam, the NASA-funded instrument onboard the Korea Aerospace Research Institute (KARI) Korea Pathfinder Lunar Orbiter (KPLO) satellite brings 200 ́more sensitive images with 1.7 m per pixel resolution. ShadowCam’s growing and publicly available dataset contains millions of previously unknown impact craters that provide important insight into various physical processes such as the impact gardening, volatile excavation, or mass wasting in lunar PSRs. In this abstract, we describe our crater detection techniques and the current state of our crater detection efforts on the ShadowCam image dataset. The first results can be found in a parallel abstract by Mazarico, Pokorny, et al. (2025).