@conference {cLuque17,
	title = {Spatio-Temporal Road Detection from Aerial Imagery using CNNs},
	booktitle = {International Conference on Computer Vision Theory and Applications},
	year = {2017},
	month = {2/2017},
	address = {Porto, Portugal},
	abstract = {<p>The main goal of this paper is to detect roads from aerial imagery recorded by drones. To achieve this, we propose a modification of SegNet, a deep fully convolutional neural network for image segmentation. In order to train this neural network, we have put together a database containing videos of roads from the point of view of a small commercial drone. Additionally, we have developed an image annotation tool based on the watershed technique, in order to perform a semi-automatic labeling of the videos in this database. The experimental results using our modified version of SegNet show a big improvement on the performance of the neural network when using aerial imagery, obtaining over 90\% accuracy.</p>},
	doi = {10.5220/0006128904930500},
	author = {Luque, B. and Morros, J.R. and Ruiz-Hidalgo, J.}
}
