A Machine Learning Based Search for Lunar Anomalies
Authors: Cameron Kelahan, Daniel Angerhausen, Adam Lesnikowski, Valentin T. Bickel
Organizations: University of Warwick, Coventry, United Kingdom CV4 7AL · Universit`a degli Studi di Padova, Via 8 Febbraio, 2 - 35122 Padova, Italy · James Madison University, 800 South Main Street, Harrisonburg, VA 22807 · The SETI Institute, 339 Bernardo Ave, Suite 200, Mountain View, CA 94043, United States · University of California, Berkeley, 910 Evans Hall, Berkeley, CA 94720 · Center for Space and Habitability, University of Bern, Gesellschaftsstrasse 6, 3012 Bern, Switzerland
The Lunar Reconnaissance Orbiter (LRO) has been collecting high-resolution images (at around 0.5-2 meters per pixel linearly with its Narrow Angle Camera) of the Moon since 2009, amassing a large dataset of images and offering researchers the opportunity to study the surface of the Moon at unprecedented scale. Here, we aim to test the abilities of the Beta-Variational Autoencoder (VAE) created by Lesnikowski et al. (2024), an unsupervised learning model which identifies anomalous features across the Moon's surface, locating not only scientifically useful geologic formations such as rockfall deposits, fresh impact craters, irregular mare patches, or volcanic pits/collapsed lava tubes, but also artificial objects such as landed spacecraft. This investigation further gauged the model's ability to locate anomalous surface features, successfully recovering two places of interest (Plaskett Crater and Paracelsus C Crater) and numerous landed technological assets at a statistically significant rate.