Abstract
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.
Highlights
For thousands of years, cartography has been an indispensable science for human society, creating maps large enough to reflect the shape and size of the earth and small enough to guide residents’ daily travel
We evaluated the generalization of the selection model using the area under the receiver operating characteristic (ROC) curve (AUC) and judged whether the spatial distribution of the roads was reasonable by considering expert selection results as a standard and performing a comparative analysis by calculating the selection accuracy and density, along with other indicators
The scores of Graph convolutional networks (GCNs) and Graph Attention Network (GAT) were as high as 90%, with the GAT model’s scores being slightly higher
Summary
Cartography has been an indispensable science for human society, creating maps large enough to reflect the shape and size of the earth and small enough to guide residents’ daily travel. With the rapid development of mobile devices and the Internet, electronic maps have become increasingly popular and useful. Roads are the main element of electronic maps, and their effective selection has always been a challenge in cartography. Roads comprise skeletal frameworks and transportation arteries that structure urban environments, and displaying them on maps directly affects the visual impression of the whole map; accurate selection of road networks is imperative. Many methods and strategies have been proposed to improve the efficiency of road network selection, which can be generally categorized as intelligent or non-intelligent. Non-intelligent selection methods are mainly based on the theory of graphs or strokes—
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