| Summary: | Abstract 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, https://doi.org/10.1029/2019ea001005) 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, https://doi.org/10.1029/2019ea001005) and Lee and Hogan (2021, https://doi.org/10.1016/j.cageo.2020.104645) 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, https://doi.org/10.1029/2019ea001005) 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.
|
|---|