SP-GEM: Spatial Pattern-Aware Graph Embedding for Matching Multisource Road Networks
Identifying correspondences of road segments in different road networks, namely road-network matching, is an essential task for road network-centric data processing such as data integration of road networks and data quality assessment of crowd-sourced road networks. Traditional road-network matching...
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MDPI AG
2025-07-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/7/275 |
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| author | Chenghao Zheng Yunfei Qiu Jian Yang Bianying Zhang Zeyuan Li Zhangxiang Lin Xianglin Zhang Yang Hou Li Fang |
| author_facet | Chenghao Zheng Yunfei Qiu Jian Yang Bianying Zhang Zeyuan Li Zhangxiang Lin Xianglin Zhang Yang Hou Li Fang |
| author_sort | Chenghao Zheng |
| collection | DOAJ |
| description | Identifying correspondences of road segments in different road networks, namely road-network matching, is an essential task for road network-centric data processing such as data integration of road networks and data quality assessment of crowd-sourced road networks. Traditional road-network matching usually relies on feature engineering and parameter selection of the geometry and topology of road networks for similarity measurement, resulting in poor performance when dealing with dense and irregular road network structures. Recent development of graph neural networks (GNNs) has demonstrated unsupervised modeling power on road network data, which learn the embedded vector representation of road networks through spatial feature induction and topology-based neighbor aggregation. However, weighting spatial information on the node feature alone fails to give full play to the expressive power of GNNs. To this end, this paper proposes a Spatial Pattern-aware Graph EMbedding learning method for road-network matching, named SP-GEM, which explores the idea of spatially-explicit modeling by identifying spatial patterns in neighbor aggregation. Firstly, a road graph is constructed from the road network data, and geometric, topological features are extracted as node features of the road graph. Then, four spatial patterns, including grid, high branching degree, irregular grid, and circuitous, are modelled in a sector-based road neighborhood for road embedding. Finally, the similarity of road embedding is used to find data correspondences between road networks. We conduct an algorithmic accuracy test to verify the effectiveness of SP-GEM on OSM and Tele Atlas data. The algorithmic accuracy experiments show that SP-GEM improves the matching accuracy and recall by at least 6.7% and 10.2% among the baselines, with high matching success rate (>70%), and improves the matching accuracy and recall by at least 17.7% and 17.0%, compared to the baseline GNNs, without spatially-explicit modeling. Further embedding analysis also verifies the effectiveness of the induction of spatial patterns. This study not only provides an effective and practical algorithm for road-network matching, but also serves as a test bed in exploring the role of spatially-explicit modeling in GNN-based road network modeling. The experimental performances of SP-GEM illuminate the path to develop GeoEmbedding services for geospatial applications. |
| format | Article |
| id | doaj-art-e723e62587834447a3e4dd3c4ff64c42 |
| institution | DOAJ |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-e723e62587834447a3e4dd3c4ff64c422025-08-20T02:45:37ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-07-0114727510.3390/ijgi14070275SP-GEM: Spatial Pattern-Aware Graph Embedding for Matching Multisource Road NetworksChenghao Zheng0Yunfei Qiu1Jian Yang2Bianying Zhang3Zeyuan Li4Zhangxiang Lin5Xianglin Zhang6Yang Hou7Li Fang8School of Software, Liaoning Technical University, Huludao 125105, ChinaSchool of Software, Liaoning Technical University, Huludao 125105, ChinaSchool of Geospatial Information, Information Engineering University, Zhengzhou 450052, ChinaChina Centre for Resources Satellite Data and Application, Beijing 100094, ChinaQuanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362216, ChinaQuanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362216, ChinaQuanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362216, ChinaQuanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362216, ChinaQuanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362216, ChinaIdentifying correspondences of road segments in different road networks, namely road-network matching, is an essential task for road network-centric data processing such as data integration of road networks and data quality assessment of crowd-sourced road networks. Traditional road-network matching usually relies on feature engineering and parameter selection of the geometry and topology of road networks for similarity measurement, resulting in poor performance when dealing with dense and irregular road network structures. Recent development of graph neural networks (GNNs) has demonstrated unsupervised modeling power on road network data, which learn the embedded vector representation of road networks through spatial feature induction and topology-based neighbor aggregation. However, weighting spatial information on the node feature alone fails to give full play to the expressive power of GNNs. To this end, this paper proposes a Spatial Pattern-aware Graph EMbedding learning method for road-network matching, named SP-GEM, which explores the idea of spatially-explicit modeling by identifying spatial patterns in neighbor aggregation. Firstly, a road graph is constructed from the road network data, and geometric, topological features are extracted as node features of the road graph. Then, four spatial patterns, including grid, high branching degree, irregular grid, and circuitous, are modelled in a sector-based road neighborhood for road embedding. Finally, the similarity of road embedding is used to find data correspondences between road networks. We conduct an algorithmic accuracy test to verify the effectiveness of SP-GEM on OSM and Tele Atlas data. The algorithmic accuracy experiments show that SP-GEM improves the matching accuracy and recall by at least 6.7% and 10.2% among the baselines, with high matching success rate (>70%), and improves the matching accuracy and recall by at least 17.7% and 17.0%, compared to the baseline GNNs, without spatially-explicit modeling. Further embedding analysis also verifies the effectiveness of the induction of spatial patterns. This study not only provides an effective and practical algorithm for road-network matching, but also serves as a test bed in exploring the role of spatially-explicit modeling in GNN-based road network modeling. The experimental performances of SP-GEM illuminate the path to develop GeoEmbedding services for geospatial applications.https://www.mdpi.com/2220-9964/14/7/275road-network matchingGNNspatially explicit modelingOSMGeoEmbeddingspatial pattern |
| spellingShingle | Chenghao Zheng Yunfei Qiu Jian Yang Bianying Zhang Zeyuan Li Zhangxiang Lin Xianglin Zhang Yang Hou Li Fang SP-GEM: Spatial Pattern-Aware Graph Embedding for Matching Multisource Road Networks ISPRS International Journal of Geo-Information road-network matching GNN spatially explicit modeling OSM GeoEmbedding spatial pattern |
| title | SP-GEM: Spatial Pattern-Aware Graph Embedding for Matching Multisource Road Networks |
| title_full | SP-GEM: Spatial Pattern-Aware Graph Embedding for Matching Multisource Road Networks |
| title_fullStr | SP-GEM: Spatial Pattern-Aware Graph Embedding for Matching Multisource Road Networks |
| title_full_unstemmed | SP-GEM: Spatial Pattern-Aware Graph Embedding for Matching Multisource Road Networks |
| title_short | SP-GEM: Spatial Pattern-Aware Graph Embedding for Matching Multisource Road Networks |
| title_sort | sp gem spatial pattern aware graph embedding for matching multisource road networks |
| topic | road-network matching GNN spatially explicit modeling OSM GeoEmbedding spatial pattern |
| url | https://www.mdpi.com/2220-9964/14/7/275 |
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