DOI: 10.5593/sgem2017/21/S08.140


M. Munko, R. Duraciova
Wednesday 13 September 2017 by Libadmin2017

References: 17th International Multidisciplinary Scientific GeoConference SGEM 2017, www.sgem.org, SGEM2017 Conference Proceedings, ISBN 978-619-7408-01-0 / ISSN 1314-2704, 29 June - 5 July, 2017, Vol. 17, Issue 21, 1105-1112 pp, DOI: 10.5593/sgem2017/21/S08.140


The main source of information needed to build, update and maintain spatial databases are aerial images. The biggest advance of aerial images is their capability to efficiently cover large areas. With the continuous development of technology, aerial imaging has become cheaper and more affordable than any time before. With this advancement of technology, large amounts of data need to be processed. In order to extract information from aerial images, they need to be processed into ortho-rectified images and then vectorized. The process of vectorization (extracting information from images and creating spatial structures) is mainly done by human operator. With the amount of the data and demand for short processing period in order to guarantee information recency, this process needs to be automated. Neural networks have proven their great usability in wide range of applications varying from stock estimation to autonomously driven vehicles. Convolutional neural networks create set of neural networks with specialization on computer vision. Their ability to correctly distinguish between multiple object presented in the images have been evaluated. We designed architecture of convolutional neural network that is suitable for road extraction from aerial images. The proposed architecture was tested in the area of city Piešťany, Slovakia. The task designed for convolutional neural network is to recognize roads in the aerial images and label pixels that are parts of the road segments. The accuracy over 85% achieved during the experiment indicates great potential in using convolutional neural networks in objects extraction from aerial images.

Keywords: roads detection, neural networks, remote sensing, computer vision

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