Automated crater detection with human level performance. (February 2021)

• Record Type: Journal Article
• Authors: Lee, Christopher; Hogan, James
• Is Part Of: Computers & geosciences. Volume 147(2021)
• Publication Date: 2021-02
• Subjects: Deep learning -- Crater Detection Algorithms -- Mars; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2020.104645
• Languages: English
• ISSNs: 0098-3004

Abstract

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. 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 . Highlights: We developed a new automated crater detection algorithm using ResUNET neural networks. The crater detection algorithm processes digital terrain model data and optical imagery data. The performance of the algorithm is on-par with expert human researchers.