Unsupervised Distribution Learning for Lunar Surface Anomaly Detection
Adam Lesnikowski, Valentin T. Bickel, Daniel Angerhausen
(Submitted on 14 Jan 2020)
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.
Comments: Second Workshop on Machine Learning and the Physical Sciences, NeurIPS 2019. Five pages, three figures
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Earth and Planetary Astrophysics (astro-ph.EP); Machine Learning (cs.LG)
ACM classes: I.2.1; I.4.9; I.2.10; J.2
Cite as: arXiv:2001.04634 [astro-ph.IM] (or arXiv:2001.04634v1 [astro-ph.IM] for this version)
Submission history
From: Adam Lesnikowski
[v1] Tue, 14 Jan 2020 05:38:37 UTC (980 KB)
