  Crater cataloging is an important yet time-consuming part of geological
mapping. We present an automated Crater Detection Algorithm (CDA) that is
competitive with expert-human researchers and hundreds of times faster. The CDA
uses multiple neural networks to process digital terrain model and thermal
infra-red imagery to identify and locate craters across the surface of Mars. We
use additional post-processing filters to refine and remove potential false
crater detections, improving our precision and recall by 10% compared to Lee
(2019). We now find 80% of known craters above 3km in diameter, and identify
7,000 potentially new craters (13% of the identified craters). The median
differences between our catalog and other independent catalogs is 2-4% in
location and diameter, in-line with other inter-catalog comparisons. The CDA
has been used to process global terrain maps and infra-red imagery for Mars,
and the software and generated global catalog are available at
https://doi.org/10.5683/SP2/CFUNII.
