Optimization of Neural Networks Classification for Disaster-Damage Assessment Using Unmanned Aerial Vehicles System

Natural disasters are recurrent weather phenomena whose occurrence has increased worldwide in the past few decades. These disasters cause devastating effects on transportation routes by causing significant damage and obstruction on frequently traveled roads. This research focuses on optimizing the classification accuracy of an autonomous network of unmanned aerial vehicles (UAVs) for transportation disaster management using convolutional neural networks (CNNs). The autonomous network of UAVs will allow first responders to optimize their rescue plans by providing relevant information on inaccessible roads. The research seeks to explore different methods to optimize the architecture of convolutional neural networks for the multiclass classification of disaster-damaged roads