Damage detection from aerial images via convolutional neural networks

This paper explores the effective use of Convolutional Neural Networks (CNNs) in the context of washed-away building detection from pre- and post-tsunami aerial images. To this end, we compile a dedicated, labeled aerial image dataset to construct models that classify whether a building is washed-away. Each datum in the set is a pair of pre- and post-tsunami image patches and encompasses a target building at the center of the patch. Using this dataset, we comprehensively evaluate CNNs from a practical-application viewpoint, e.g., input scenarios (pre-tsunami images are not always available), input scales (building size varies) and different configurations for CNNs. The experimental results show that our CNN-based washed-away detection system achieves 94-96% classification accuracy across all conditions, indicating the promising applicability of CNNs for washed-away building detection.