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Authors: Amanda Spolti 1 ; Vitor C. Guizilini 2 ; Caio C. T. Mendes 3 ; Matheus D. Croce 4 ; André R. R. de Geus 1 ; Henrique C. Oliveira 5 ; André R. Backes 1 and Jefferson R. Souza 1

Affiliations: 1 School of Computer Science, Federal University of Uberlândia, Uberlândia, MG, Brazil ; 2 School of Information Technologies, University of Sydney, Sydney, Australia ; 3 Department of Computer Science, Federal University of São Carlos, São Carlos, SP, Brazil ; 4 Institute of Mathematics and Computer Science, University of São Paulo, São Paulo, SP, Brazil ; 5 Faculty of Civil Engineering, Architecture and Urbanism, State University of Campinas, Campinas, SP, Brazil

Keyword(s): Road Detection, Deep Learning, Auto-Enconder, U-Net.

Abstract: One of the challenges in extracting road network from aerial images is an enormous amount of different cartographic features interacting with each other. This paper presents a methodology to detect the road network from aerial images. The methodology applies a Deep Learning (DL) architecture named U-Net and a fully convolutional Auto-Encoder for comparison. High-resolution RGB images of an urban area were obtained from a conventional photogrammetric mission. The experiments show that both architectures achieve satisfactory results for detecting road network while maintaining low inference time once DL networks are trained.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Spolti, A.; Guizilini, V.; Mendes, C.; Croce, M.; R. de Geus, A.; Oliveira, H.; Backes, A. and Souza, J. (2020). Application of U-Net and Auto-Encoder to the Road/Non-road Classification of Aerial Imagery in Urban Environments. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 607-614. DOI: 10.5220/0009337306070614

@conference{visapp20,
author={Amanda Spolti. and Vitor C. Guizilini. and Caio C. T. Mendes. and Matheus D. Croce. and André R. {R. de Geus}. and Henrique C. Oliveira. and André R. Backes. and Jefferson R. Souza.},
title={Application of U-Net and Auto-Encoder to the Road/Non-road Classification of Aerial Imagery in Urban Environments},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={607-614},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009337306070614},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Application of U-Net and Auto-Encoder to the Road/Non-road Classification of Aerial Imagery in Urban Environments
SN - 978-989-758-402-2
IS - 2184-4321
AU - Spolti, A.
AU - Guizilini, V.
AU - Mendes, C.
AU - Croce, M.
AU - R. de Geus, A.
AU - Oliveira, H.
AU - Backes, A.
AU - Souza, J.
PY - 2020
SP - 607
EP - 614
DO - 10.5220/0009337306070614
PB - SciTePress

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