Shadows in aerial images can hinder the performance of various vision tasks, including object detection and tracking. Shadow detection networks see a reduction in performance in mid-altitude wide area motion imagery (WAMI) data since they lack the related data for training. Aerial WAMI data collection is a challenging task, and the variety of weather conditions that can be captured is limited. Moreover, obtaining accurate ground truth shadow masks for these images is difficult, where manual methods are infeasible and automatic techniques suffer from inaccuracies. We are leveraging the advanced rendering capabilities of Unreal Engine to produce city-scale synthetic aerial images. Unreal Engine can provide precise ground-truth shadow masks and cover diverse weather and lighting conditions. We further train and evaluate an existing shadow detection network with our synthetic data to improve the performance on real WAMI datasets.
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