scholarly journals BasqueRoads: A Benchmark for Road Network Selection

2022 ◽  
Vol 4 ◽  
pp. 1-2
Author(s):  
Guillaume Touya ◽  
Azelle Courtial
Keyword(s):  
Road Network ◽  
Network Selection

scholarly journals Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection

2021 ◽  
Vol 10 (11) ◽  
pp. 768
Author(s):  
Jing Zheng ◽  
Ziren Gao ◽  
Jingsong Ma ◽  
Jie Shen ◽  
Kang Zhang
Keyword(s):  
Road Network ◽  
Spatial Information ◽  
Road Networks ◽  
Selection Effect ◽  
Network Selection ◽  
Small Scale ◽  
Attention Networks ◽  
Convolutional Networks ◽  
Spatial Features ◽  
Selection Of

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.

Planning a Backbone Road Network in Scenic Areas Using the Importance Method

ICCTP 2010 ◽  
2010 ◽  
Author(s):  
Chun-peng Han ◽  
Pei-feng Cheng
Keyword(s):  
Road Network

The Cooperative Guidance Path Optimization Based on Road Network Layered

CICTP 2019 ◽  
2019 ◽  
Author(s):  
Zhi-Lin Li ◽  
Ming Wang ◽  
Shu-Tao Gao
Keyword(s):  
Road Network ◽  
Path Optimization ◽  
Cooperative Guidance
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