Automated crater detection with human level performance.

Abstract:

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. In Lee (2019) we trained a model with 72-75% recall and precision on global Mars datasets for craters down to 3km in diameter. With additional post-processing filters to refine and remove potential false crater detections, we improve the precision and recall by 10% compared to Lee (2019), finding 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://dataverse.scholarsportal.info/dataverse/lee