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2019 | OriginalPaper | Buchkapitel

Delineation of Road Networks Using Deep Residual Neural Networks and Iterative Hough Transform

verfasst von : Pinjing Xu, Charalambos Poullis

Erschienen in: Advances in Visual Computing

Verlag: Springer International Publishing

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Abstract

In this paper we present a complete pipeline for extracting road network vector data from satellite RGB orthophotos of urban areas. Firstly, a network based on the SegNeXt architecture with a novel loss function is employed for the semantic segmentation of the roads. Results show that the proposed network produces on average better results than other state-of-the-art semantic segmentation techniques. Secondly, we propose a fast post-processing technique for vectorizing the rasterized segmentation result, removing erroneous lines, and refining the road network. The result is a set of vectors representing the road network. We have extensively tested the proposed pipeline and provide quantitative and qualitative comparisons with other state-of-the-art based on a number of known metrics.

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Vorheriges Kapitel DeepGRU: Deep Gesture Recognition Utility

Nächstes Kapitel DomainSiam: Domain-Aware Siamese Network for Visual Object Tracking

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Titel: Delineation of Road Networks Using Deep Residual Neural Networks and Iterative Hough Transform
verfasst von: Pinjing Xu
Charalambos Poullis
Verlag: Springer International Publishing
Buch: Advances in Visual Computing
Print ISBN: 978-3-030-33719-3

Electronic ISBN: 978-3-030-33720-9

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-33720-9_3

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