  In this work we show that modern data-driven machine learning techniques can
be successfully applied on lunar surface remote sensing data to learn, in an
unsupervised way, sufficiently good representations of the data distribution to
enable lunar technosignature and anomaly detection. In particular we train an
unsupervised distribution learning neural network model to find the Apollo 15
landing module in a testing dataset, with no dataset specific model or
hyperparameter tuning. Sufficiently good unsupervised data density estimation
has the promise of enabling myriad useful downstream tasks, including locating
lunar resources for future space flight and colonization, finding new impact
craters or lunar surface reshaping, and algorithmically deciding the importance
of unlabeled samples to send back from power- and bandwidth-constrained
missions. We show in this work that such unsupervised learning can be
successfully done in the lunar remote sensing and space science contexts.
