Hurricane Damage Detection From Satellite Imagery Using Convolutional Neural Networks

Abstract

Hurricanes are one of the most disastrous natural phenomena occurring on Earth that cause loss of human lives and immense damage to property as well. For assessment of this damage, windshield survey is commonly used, which is an error-prone and time-consuming method. For solving this problem, computer vision comes into the picture. In this paper, a convolutional neural network-based architecture has been proposed to classify the post-hurricane satellite imagery into damaged and undamaged building classes accurately. The model consists of five convolutional and five pooling layers followed by a flattening layer and two dense layers. For this, a dataset of Hurricane Harvey has been considered having 23000 satellite images each of size 128 X 128 pixels. With the proposed model, the author has achieved an accuracy of 92.91%, F1-score of 93%, sensitivity of 93.34%, specificity of 92.47%, and precision of 92.65% at a learning rate of 0.0001 and 30 epochs. Also, low false positive rate of 7.53% and false negative rate of 6.66% were obtained.