Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection

The selection of road networks is very important for cartographic generalization. Traditional artificial intelligence methods have improved selection efficiency but cannot fully extract the spatial features of road networks. However, current selection methods, which are based on the theory of graphs...

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Autores principales:	Jing Zheng, Ziren Gao, Jingsong Ma, Jie Shen, Kang Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	road network selection graph convolutional networks (GCNs) deep architectures cartographic generalization Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/228e322f366f4c72b9d81525a0efd174
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spelling	oai:doaj.org-article:228e322f366f4c72b9d81525a0efd1742021-11-25T17:53:06ZDeep Graph Convolutional Networks for Accurate Automatic Road Network Selection10.3390/ijgi101107682220-9964https://doaj.org/article/228e322f366f4c72b9d81525a0efd1742021-11-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/768https://doaj.org/toc/2220-9964The selection of road networks is very important for cartographic generalization. Traditional artificial intelligence methods have improved selection efficiency but cannot fully extract the spatial features of road networks. However, current selection methods, which are based on the theory of graphs or strokes, have low automaticity and are highly subjective. Graph convolutional networks (GCNs) combine graph theory with neural networks; thus, they can not only extract spatial information but also realize automatic selection. Therefore, in this study, we adopted GCNs for automatic road network selection and transformed the process into one of node classification. In addition, to solve the problem of gradient vanishing in GCNs, we compared and analyzed the results of various GCNs (GraphSAGE and graph attention networks [GAT]) by selecting small-scale road networks under different deep architectures (JK-Nets, ResNet, and DenseNet). Our results indicate that GAT provides better selection of road networks than other models. Additionally, the three abovementioned deep architectures can effectively improve the selection effect of models; JK-Nets demonstrated more improvement with higher accuracy (88.12%) than other methods. Thus, our study shows that GCN is an appropriate tool for road network selection; its application in cartography must be further explored.Jing ZhengZiren GaoJingsong MaJie ShenKang ZhangMDPI AGarticleroad network selectiongraph convolutional networks (GCNs)deep architecturescartographic generalizationGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 768, p 768 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	road network selection graph convolutional networks (GCNs) deep architectures cartographic generalization Geography (General) G1-922
spellingShingle	road network selection graph convolutional networks (GCNs) deep architectures cartographic generalization Geography (General) G1-922 Jing Zheng Ziren Gao Jingsong Ma Jie Shen Kang Zhang Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection
description	The selection of road networks is very important for cartographic generalization. Traditional artificial intelligence methods have improved selection efficiency but cannot fully extract the spatial features of road networks. However, current selection methods, which are based on the theory of graphs or strokes, have low automaticity and are highly subjective. Graph convolutional networks (GCNs) combine graph theory with neural networks; thus, they can not only extract spatial information but also realize automatic selection. Therefore, in this study, we adopted GCNs for automatic road network selection and transformed the process into one of node classification. In addition, to solve the problem of gradient vanishing in GCNs, we compared and analyzed the results of various GCNs (GraphSAGE and graph attention networks [GAT]) by selecting small-scale road networks under different deep architectures (JK-Nets, ResNet, and DenseNet). Our results indicate that GAT provides better selection of road networks than other models. Additionally, the three abovementioned deep architectures can effectively improve the selection effect of models; JK-Nets demonstrated more improvement with higher accuracy (88.12%) than other methods. Thus, our study shows that GCN is an appropriate tool for road network selection; its application in cartography must be further explored.
format	article
author	Jing Zheng Ziren Gao Jingsong Ma Jie Shen Kang Zhang
author_facet	Jing Zheng Ziren Gao Jingsong Ma Jie Shen Kang Zhang
author_sort	Jing Zheng
title	Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection
title_short	Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection
title_full	Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection
title_fullStr	Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection
title_full_unstemmed	Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection
title_sort	deep graph convolutional networks for accurate automatic road network selection
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/228e322f366f4c72b9d81525a0efd174
work_keys_str_mv	AT jingzheng deepgraphconvolutionalnetworksforaccurateautomaticroadnetworkselection AT zirengao deepgraphconvolutionalnetworksforaccurateautomaticroadnetworkselection AT jingsongma deepgraphconvolutionalnetworksforaccurateautomaticroadnetworkselection AT jieshen deepgraphconvolutionalnetworksforaccurateautomaticroadnetworkselection AT kangzhang deepgraphconvolutionalnetworksforaccurateautomaticroadnetworkselection
_version_	1718411851041079296

Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection

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