Automatic Bomb Crater Detection and Localization Based on Historical Aerial Imagery

More than 70 years after the end of World War II, remaining military material and ordnance is still a problem in many European countries. Unexploded bombs left in the ground pose a particularly serious hazard, especially in heavily bombarded areas. Given the danger of accidental triggering of unexploded bombs, careful examination is required before construction or other activities can be permitted. For this purpose, bomb disposal teams analyze historical aerial reconnaissance imagery from World War II to evaluate risks. This is done mostly manually via 2D and 3D visual inspection and GIS. For each examination, a large number of images has to be evaluated. A (semi-)automation of procedures is therefore of great interest.We present preliminary results of an end-to-end process using Convolutional Neural Networks (CNNs) we propose for automatic detection and localization of bomb craters and impacts in historical aerial imagery. The goal is to create a service that can help bomb disposal teams to assess risks fast and reliably through a synergistic use of human and AI capacities.