# Automated crater detection with human level performance

arXiv: https://arxiv.org/abs/2010.12520 · HTML: https://mlsanity.com/paper/2010.12520/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Deep Learning based Systems for Crater Detection: A Review](https://mlsanity.com/paper/2310.07727/) (2023)
- [Vision-Language Model for Accurate Crater Detection](https://mlsanity.com/paper/2601.07795/) (2026)
- [Camera-Pose Robust Crater Detection from Chang'e 5](https://mlsanity.com/paper/2406.04569/) (2024)
- [Comparison of automated crater catalogs for Mars from Benedix et al. (2020) and Lee and Hogan (2021)](https://mlsanity.com/paper/2308.14650/) (2023)

## Abstract (arXiv)

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.