A Method for Intelligent Road Network Selection Based on Graph Neural Network

As an essential role in cartographic generalization, road network selection produces basic geographic information across map scales. However, the previous selection methods could not simultaneously consider both attribute characteristics and spatial structure. In light of this, an intelligent road n...

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Bibliographic Details
Published in:ISPRS international journal of geo-information 2023-08, Vol.12 (8), p.336
Main Authors: Guo, Xuan, Liu, Junnan, Wu, Fang, Qian, Haizhong
Format: Article
Language:English
Subjects:
Aggregation
Algorithms
Artificial intelligence
Big Data
Bridges
cartographic generalization
Cartography
Classification
Cognition & reasoning
Comparative analysis
Connectivity
Deep learning
Geospatial data
graph neural network
Graph neural networks
Machine learning
Methods
Neural networks
road network selection
Roads
Roads & highways
Semantics
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Summary:As an essential role in cartographic generalization, road network selection produces basic geographic information across map scales. However, the previous selection methods could not simultaneously consider both attribute characteristics and spatial structure. In light of this, an intelligent road network selection method based on a graph neural network (GNN) is proposed in this paper. Firstly, the selection case is designed to construct a sample library. Secondly, some neighbor sampling and aggregation rules are developed to update road features. Then, a GNN-based selection model is designed to calculate classification labels, thus completing road network selection. Finally, a few comparative analyses with different selection methods are conducted, verifying that most of the accuracy values of the GNN model are stable over 90%. The experiments indicate that this method could aggregate stroke nodes and their neighbors together to synchronously preserve semantic, geometric, and topological features of road strokes, and the selection result is closer to the reference map. Therefore, this paper could bridge the distance between deep learning and cartographic generalization, thus facilitating a more intelligent road network selection method.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi12080336