4 citations · 4 across the 3 of their papers we have counts for
3 papers
A Machine Learning Based Search for Lunar Anomalies
Cameron Kelahan, Daniel Angerhausen, Adam Lesnikowski +1
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, a…
Machine Learning as a Transformative Tool for (Exo-)Planetary Science
J. Davoult, V. T. Bickel, C. Haslebacher +11
The exploration of planetary bodies in our Solar system and beyond relies on the processing and interpretation of large, spatio-temporally inconsistent, and heterogeneous datasets.…
Unsupervised Distribution Learning for Lunar Surface Anomaly Detection
Adam Lesnikowski, Valentin T. Bickel, Daniel Angerhausen
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, sufficie…