Abstract
Especially in recent years, studies to determine the effects of natural disasters from satellite images have been very popular. The destruction caused by the disaster and the early detection of the affected structures are of great importance for the establishment of the precautionary measures and the right action plan. However, studies in this area are mostly made observationally and as a result, desired results cannot be achieved. On the other hand, the introduction of machine learning-based detection methods is very promising. In this study, a damaged building detection method based on convolutional neural networks (CNN) is proposed. Unlike similar studies, the hyperparameters of the CNN are optimized using Bayesian optimization algorithm to obtain more accurate and reliable detection results. The testing and validation results performed with a large number of images reveal the robustness of the proposed method. In addition, the performance evaluation measures obtained from the balanced and unbalanced testing datasets solidified the success of the optimized CNN model.
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