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Crater mapping using neural networks and other automated methods hasincreased recently with automated Crater Detection Algorithms (CDAs) applied toplanetary bodies throughout the solar system. A recent publication by Benedixet al. (2020) showed high performance at small scales compared to similarautomated 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 metricsused to test the catalogs. I show how the more permissive comparison methodsindicate a higher CDA performance by allowing worse candidate craters to matchground-truth craters. I show that the Benedix et al. (2020) catalog has asubstantial performance loss with increasing latitude and identify an imageprojection issue that might cause this loss. Finally, I suggest futureapplications of neural networks in generating large scientific datasets bevalidated using secondary networks with independent data sources or trainingmethods.

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