Automated crater detection with human level performance

• URL: http://arxiv.org/abs/2010.12520v2
• Date: Wed, 18 Nov 2020 05:20:47 GMT
• Title: Automated crater detection with human level performance
• Authors: Christopher Lee, James Hogan
• Abstract summary: We present an automated Crater Detection Algorithm that is competitive with expert-human researchers and hundreds of times faster. The algorithm 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 find 80% of known craters above 3km in diameter, and identify 7,000 potentially new craters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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