Name
Abstract | ||
|---|---|---|
Monitoring of disasters is crucial for mitigating their effects on the environment and human population, and can be facilitated by the use of unmanned aerial vehicles (UAV), equipped with camera sensors that produce aerial photos of the areas of interest. A modern 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 identification. We demonstrate the potential of this technique in identifying disasters with high accuracy, by means of a relatively simple deep learning model. Based on a dataset of 544 images (containing disaster images such as fires, earthquakes, collapsed buildings, tsunami and flooding, as well as non-disaster scenes), our 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. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Population,Disaster monitoring,Image sensor,Real-time computing,Artificial intelligence,Deep learning,Flooding (psychology),Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1807.11805 | 1 |
PageRank | References | Authors |
0.34 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Kamilaris | 1 | 206 | 15.70 |
| Francesc X. Prenafeta-Boldu | 2 | 90 | 4.36 |