Deep Convolution Neural Network for Automated Method of Road Extraction on Aerial Imagery


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Output type: Book

UM6P affiliated Publication?: Yes

Author list: Abderrahim N., Saadane A., Rida A.

Edition name or number: Geospatial Intelligence

Publisher: springer

Publication year: 2022

Title of series: Advances in Science, Technology & Innovation

Start page: 31

End page: 40

ISBN: 2522-8714

eISBN: 2522-8722

ISSN: 2522-8714

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119138255&doi=10.1007%2f978-3-030-80458-9_3&partnerID=40&md5=90db9de479d34fc8759e7dd70d79be63

Languages: English (EN-GB)


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Abstract

The detection of roads from satellite images is among the most important topics for planning and development for cities, which replaces manual methods, but they turn out to be a complex task due to the complexity of the objects. This paper discusses issues related to the detection and segmentation of roads in very high-resolution aerial images. In order to resolve these issues, we propose in this paper the use, deployment, and validation of deep learning strategies, in particular, the U-net architecture based on deep convolutional neural networks for extracting roads from remote sensing images. Data augmentation techniques and preprocessing were applied to improve accuracy. We use in our processing for road segmentation the Massachusetts Road Dataset, which is publicly available. The results obtained showed excellent performance in terms of recall, precision, accuracy, and F1 score, and they are very close to the ground truth; it outperforms all other models presented, with a high accuracy of 97.7%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.


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Last updated on 2022-17-01 at 23:16