Convolutional Neural Network-Based Disaster Assessment Using Unmanned Aerial Vehicles

Abstract

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 developing 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 autonomous UAV system development will increase the affected regions' recovery rate by identifying blocked transportation routes and associating them with their corresponding locations to update the virtual map in real-time. Live footage from the unmanned aerial vehicles is fed to ground control, where the CNN classifies the type of damage encountered and then updates a virtual map through the ArcGIs software. Preliminary results of the classification models such as AlexNet show average accuracy of 74.07%. Furthermore, transfer learning and cross-validation techniques were applied to the CNN models to obtain high confidence levels due to the small dataset size used to train and test the CNNs. To choose the best CNN model, a quantitative analysis was performed to measure the statistical precision, statistical recall, and F1 score on each model to optimize the classification.