  The NASA Lunar Reconnaissance Orbiter (LRO) has returned petabytes of lunar
high spatial resolution surface imagery over the past decade, impractical for
humans to fully review manually. Here we develop an automated method using a
deep generative visual model that rapidly retrieves scientifically interesting
examples of LRO surface imagery representing the first planetary image anomaly
detector. We give quantitative experimental evidence that our method
preferentially retrieves anomalous samples such as notable geological features
and known human landing and spacecraft crash sites. Our method addresses a
major capability gap in planetary science and presents a novel way to unlock
insights hidden in ever-increasing remote sensing data archives, with numerous
applications to other science domains. We publish our code and data along with
this paper.
