Application of convolutional neural networks for detection of damaged buildings

Abstract

The paper describes an approach to solving the problem of detection damaged buildings on satellite or other images using convolutional neural networks. To solve the problem, the U-Net convolutional network architecture was chosen. For the study, a proprietary data set containing 50 images with dimensions of 512x512 pixels was used. The application of augmentations was considered to increase the variability of the data set, which made it possible to train the neural network on a small number of images, which had a positive effect on further results. 5 different models of the U-Net architecture were built, the impact of various parameters on the effectiveness of the models was investigated. It has been proven that the initial number of filters has a positive effect on the accuracy of the model. Improved segmentation accuracy for damaged and undamaged buildings. The proposed approach makes it possible to make a preliminary assessment of the degree of damage of buildings and contributes to the implementation of recognition systems based on convolutional neural networks for solving practical tasks.