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The aftermath of air raids can still be seen for decades after thedevastating events. Unexploded ordnance (UXO) is an immense danger to humanlife and the environment. Through the assessment of wartime images, experts caninfer the occurrence of a dud. The current manual analysis process is expensiveand time-consuming, thus automated detection of bomb craters by using deeplearning is a promising way to improve the UXO disposal process. However, thesemethods require a large amount of manually labeled training data. This workleverages domain adaptation with moon surface images to address the problem ofautomated bomb crater detection with deep learning under the constraint oflimited training data. This paper contributes to both academia and practice (1)by providing a solution approach for automated bomb crater detection withlimited training data and (2) by demonstrating the usability and associatedchallenges of using synthetic images for domain adaptation.

Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images