A road generalization method using graph convolutional network based on mesh-line structure unit
Road network simplification is a complex decision-making process. Such a multi-factor decision and scaling operation traditionally applied rule-based methods. The establishment and adjustment of these rules involve many human-set parameters and conditions, which makes generalized results closely rel...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-01-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2024.2413549 |
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| _version_ | 1850245783059693568 |
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| author | Tianyuan Xiao Tinghua Ai Dirk Burghardt Pengcheng Liu Min Yang Aji Gao Bo Kong Huafei Yu |
| author_facet | Tianyuan Xiao Tinghua Ai Dirk Burghardt Pengcheng Liu Min Yang Aji Gao Bo Kong Huafei Yu |
| author_sort | Tianyuan Xiao |
| collection | DOAJ |
| description | Road network simplification is a complex decision-making process. Such a multi-factor decision and scaling operation traditionally applied rule-based methods. The establishment and adjustment of these rules involve many human-set parameters and conditions, which makes generalized results closely related to the cartographer’s experience and habits. On the other hand, existing methods tend to consider individual structures separately in different algorithms, such as strokes, meshes and graph networks, lacking a solution that brings the advantages of these methods together. Aiming at the above problems, this study designs a simplification method using the Mesh-Line Structure Unit (MLSU) to consider polyline and polygon characteristics simultaneously with the support of graph-based deep learning networks. In order to make generalization decisions, a model based on graph convolutional network (GCN) is constructed and trained using real data, thus realizing the road network selective omission. The experimental results indicate that the proposed method effectively achieves automatic road generalization. |
| format | Article |
| id | doaj-art-fd5c3f3c7ada4b73a824c1b670a60f70 |
| institution | OA Journals |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-fd5c3f3c7ada4b73a824c1b670a60f702025-08-20T01:59:21ZengTaylor & Francis GroupGeocarto International1010-60491752-07622024-01-0139110.1080/10106049.2024.2413549A road generalization method using graph convolutional network based on mesh-line structure unitTianyuan Xiao0Tinghua Ai1Dirk Burghardt2Pengcheng Liu3Min Yang4Aji Gao5Bo Kong6Huafei Yu7School of Resource and Environment Sciences, Wuhan University, Wuhan, ChinaSchool of Resource and Environment Sciences, Wuhan University, Wuhan, ChinaInstitute of Cartography, Technische Universität Dresden, Dresden, GermanyCollege of Urban and Environmental Sciences, Central China Normal University, Wuhan, ChinaSchool of Resource and Environment Sciences, Wuhan University, Wuhan, ChinaSchool of Resource and Environment Sciences, Wuhan University, Wuhan, ChinaSchool of Resource and Environment Sciences, Wuhan University, Wuhan, ChinaSchool of Resource and Environment Sciences, Wuhan University, Wuhan, ChinaRoad network simplification is a complex decision-making process. Such a multi-factor decision and scaling operation traditionally applied rule-based methods. The establishment and adjustment of these rules involve many human-set parameters and conditions, which makes generalized results closely related to the cartographer’s experience and habits. On the other hand, existing methods tend to consider individual structures separately in different algorithms, such as strokes, meshes and graph networks, lacking a solution that brings the advantages of these methods together. Aiming at the above problems, this study designs a simplification method using the Mesh-Line Structure Unit (MLSU) to consider polyline and polygon characteristics simultaneously with the support of graph-based deep learning networks. In order to make generalization decisions, a model based on graph convolutional network (GCN) is constructed and trained using real data, thus realizing the road network selective omission. The experimental results indicate that the proposed method effectively achieves automatic road generalization.https://www.tandfonline.com/doi/10.1080/10106049.2024.2413549Map generalizationroad networkGCNmesh-line structure unit |
| spellingShingle | Tianyuan Xiao Tinghua Ai Dirk Burghardt Pengcheng Liu Min Yang Aji Gao Bo Kong Huafei Yu A road generalization method using graph convolutional network based on mesh-line structure unit Geocarto International Map generalization road network GCN mesh-line structure unit |
| title | A road generalization method using graph convolutional network based on mesh-line structure unit |
| title_full | A road generalization method using graph convolutional network based on mesh-line structure unit |
| title_fullStr | A road generalization method using graph convolutional network based on mesh-line structure unit |
| title_full_unstemmed | A road generalization method using graph convolutional network based on mesh-line structure unit |
| title_short | A road generalization method using graph convolutional network based on mesh-line structure unit |
| title_sort | road generalization method using graph convolutional network based on mesh line structure unit |
| topic | Map generalization road network GCN mesh-line structure unit |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2024.2413549 |
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