Application of U-Net and Auto-Encoder to the Road/Non-road Classification of Aerial Imagery in Urban Environments

Amanda Spolti, Vitor C. Guizilini, Caio C. T. Mendes, Matheus D. Croce, André R. Backes, Henrique C. Oliveira, André R. Backes, Jefferson R. Souza

2020

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.

Paper Citation

in Harvard Style

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, SciTePress, pages 607-614. DOI: 10.5220/0009337306070614

in Bibtex Style

@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},
}

in EndNote Style

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
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