# Comparison of automated crater catalogs for Mars from Benedix et al. (2020) and Lee and Hogan (2021)

arXiv: https://arxiv.org/abs/2308.14650 · HTML: https://mlsanity.com/paper/2308.14650/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 28 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data](https://mlsanity.com/paper/1904.00592/) (2019)
- [Deep learning universal crater detection using Segment Anything Model (SAM)](https://mlsanity.com/paper/2304.07764/) (2023)
- [Automated crater detection with human level performance](https://mlsanity.com/paper/2010.12520/) (2020)
- [Autonomous crater detection on asteroids using a fully-convolutional neural network](https://mlsanity.com/paper/2204.00477/) (2022)

## Abstract (arXiv)

Crater mapping using neural networks and other automated methods has increased recently with automated Crater Detection Algorithms (CDAs) applied to planetary bodies throughout the solar system. A recent publication by Benedix et al. (2020) showed high performance at small scales compared to similar automated CDAs but with a net positive diameter bias in many crater candidates. I compare the publicly available catalogs from Benedix et al. (2020) and Lee & Hogan (2021) and show that the reported performance is sensitive to the metrics used to test the catalogs. I show how the more permissive comparison methods indicate a higher CDA performance by allowing worse candidate craters to match ground-truth craters. I show that the Benedix et al. (2020) catalog has a substantial performance loss with increasing latitude and identify an image projection issue that might cause this loss. Finally, I suggest future applications of neural networks in generating large scientific datasets be validated using secondary networks with independent data sources or training methods.