Disaster Monitoring using Unmanned Aerial Vehicles and Deep Learning

Monitoring and identification of disasters are crucial for mitigating their effects on the environment and on human population, and can be facilitated by the use of unmanned aerial vehicles (UAV), equipped with camera sensors which can produce frequent aerial photos of the areas of interest. A modern, promising technique for recognition of events based on aerial photos is deep learning. In this paper, we present the state of the art work related to the use of deep learning techniques for disaster monitoring and identification. Moreover, we demonstrate the potential of this technique in identifying disasters automatically, with high accuracy, by means of a relatively simple deep learning model. Based on a small dataset of 544 images (containing images of disasters such as fires, earthquakes, collapsed buildings, tsunami and flooding, as well as “non-disaster” scenes), our preliminary results show an accuracy of 91% achieved, indicating that deep learning, combined with UAV equipped with camera sensors, have the potential to predict disasters with high accuracy in the near future.