{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T04:24:36Z","timestamp":1774499076604,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42130112"],"award-info":[{"award-number":["42130112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42371479"],"award-info":[{"award-number":["42371479"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2017YFB0503500"],"award-info":[{"award-number":["2017YFB0503500"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"China\u2019s National Key R&amp;D Program","award":["42130112"],"award-info":[{"award-number":["42130112"]}]},{"name":"China\u2019s National Key R&amp;D Program","award":["42371479"],"award-info":[{"award-number":["42371479"]}]},{"name":"China\u2019s National Key R&amp;D Program","award":["2017YFB0503500"],"award-info":[{"award-number":["2017YFB0503500"]}]},{"name":"KartoBit Research Network","award":["42130112"],"award-info":[{"award-number":["42130112"]}]},{"name":"KartoBit Research Network","award":["42371479"],"award-info":[{"award-number":["42371479"]}]},{"name":"KartoBit Research Network","award":["2017YFB0503500"],"award-info":[{"award-number":["2017YFB0503500"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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 (&gt;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.<\/jats:p>","DOI":"10.3390\/ijgi14070275","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T15:01:05Z","timestamp":1752591665000},"page":"275","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SP-GEM: Spatial Pattern-Aware Graph Embedding for Matching Multisource Road Networks"],"prefix":"10.3390","volume":"14","author":[{"given":"Chenghao","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Software, Liaoning Technical University, Huludao 125105, China"},{"name":"Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362216, China"}]},{"given":"Yunfei","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Software, Liaoning Technical University, Huludao 125105, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2373-8799","authenticated-orcid":false,"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450052, China"}]},{"given":"Bianying","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Centre for Resources Satellite Data and Application, Beijing 100094, China"}]},{"given":"Zeyuan","family":"Li","sequence":"additional","affiliation":[{"name":"Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362216, China"}]},{"given":"Zhangxiang","family":"Lin","sequence":"additional","affiliation":[{"name":"Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362216, China"}]},{"given":"Xianglin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362216, China"}]},{"given":"Yang","family":"Hou","sequence":"additional","affiliation":[{"name":"Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362216, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0969-4083","authenticated-orcid":false,"given":"Li","family":"Fang","sequence":"additional","affiliation":[{"name":"Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362216, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"ref_1","first-page":"714","article-title":"Hierarchical semantic similarity metric model oriented to road network matching","volume":"25","author":"Wang","year":"2023","journal-title":"J. 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