  The aftermath of air raids can still be seen for decades after the
devastating events. Unexploded ordnance (UXO) is an immense danger to human
life and the environment. Through the assessment of wartime images, experts can
infer the occurrence of a dud. The current manual analysis process is expensive
and time-consuming, thus automated detection of bomb craters by using deep
learning is a promising way to improve the UXO disposal process. However, these
methods require a large amount of manually labeled training data. This work
leverages domain adaptation with moon surface images to address the problem of
automated bomb crater detection with deep learning under the constraint of
limited training data. This paper contributes to both academia and practice (1)
by providing a solution approach for automated bomb crater detection with
limited training data and (2) by demonstrating the usability and associated
challenges of using synthetic images for domain adaptation.
