Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network
Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research toward the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of simi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-08-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/13/9/300 |
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| author | Haohua Zheng Jianchen Zhang Heying Li Guangxia Wang Jianzhong Guo Jiayao Wang |
| author_facet | Haohua Zheng Jianchen Zhang Heying Li Guangxia Wang Jianzhong Guo Jiayao Wang |
| author_sort | Haohua Zheng |
| collection | DOAJ |
| description | Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research toward the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where the feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another for extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we use a set of metrics considering both quantitative and qualitative aspects of the road network. On road types with similar sample sizes, the HAN model outperforms other models in both transductive and inductive tasks. Its accuracy (ACC) is higher by 1.62% and 0.67%, and its F1-score is higher by 1.43% and 0.81%, respectively. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation. |
| format | Article |
| id | doaj-art-7c8b785d339e41d3a339d065eebcb6da |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-7c8b785d339e41d3a339d065eebcb6da2025-08-20T01:55:33ZengMDPI AGISPRS International Journal of Geo-Information2220-99642024-08-0113930010.3390/ijgi13090300Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural NetworkHaohua Zheng0Jianchen Zhang1Heying Li2Guangxia Wang3Jianzhong Guo4Jiayao Wang5College of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaCollege of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaCollege of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaCollege of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaCollege of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaCollege of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaSelecting road networks in cartographic generalization has consistently posed formidable challenges, driving research toward the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where the feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another for extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we use a set of metrics considering both quantitative and qualitative aspects of the road network. On road types with similar sample sizes, the HAN model outperforms other models in both transductive and inductive tasks. Its accuracy (ACC) is higher by 1.62% and 0.67%, and its F1-score is higher by 1.43% and 0.81%, respectively. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.https://www.mdpi.com/2220-9964/13/9/300cartographic generalizationroad network selectionHeterogeneous Graph Attention Networkmeta-path |
| spellingShingle | Haohua Zheng Jianchen Zhang Heying Li Guangxia Wang Jianzhong Guo Jiayao Wang Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network ISPRS International Journal of Geo-Information cartographic generalization road network selection Heterogeneous Graph Attention Network meta-path |
| title | Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network |
| title_full | Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network |
| title_fullStr | Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network |
| title_full_unstemmed | Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network |
| title_short | Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network |
| title_sort | road network intelligent selection method based on heterogeneous graph attention neural network |
| topic | cartographic generalization road network selection Heterogeneous Graph Attention Network meta-path |
| url | https://www.mdpi.com/2220-9964/13/9/300 |
| work_keys_str_mv | AT haohuazheng roadnetworkintelligentselectionmethodbasedonheterogeneousgraphattentionneuralnetwork AT jianchenzhang roadnetworkintelligentselectionmethodbasedonheterogeneousgraphattentionneuralnetwork AT heyingli roadnetworkintelligentselectionmethodbasedonheterogeneousgraphattentionneuralnetwork AT guangxiawang roadnetworkintelligentselectionmethodbasedonheterogeneousgraphattentionneuralnetwork AT jianzhongguo roadnetworkintelligentselectionmethodbasedonheterogeneousgraphattentionneuralnetwork AT jiayaowang roadnetworkintelligentselectionmethodbasedonheterogeneousgraphattentionneuralnetwork |