{"title":"基于异构图注意神经网络的路网智能选择方法","authors":"Haohua Zheng, Jianchen Zhang, Heying Li, Guangxia Wang, Jianzhong Guo, Jiayao Wang","doi":"10.3390/ijgi13090300","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"22 1","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network\",\"authors\":\"Haohua Zheng, Jianchen Zhang, Heying Li, Guangxia Wang, Jianzhong Guo, Jiayao Wang\",\"doi\":\"10.3390/ijgi13090300\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":48738,\"journal\":{\"name\":\"ISPRS International Journal of Geo-Information\",\"volume\":\"22 1\",\"pages\":\"\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-08-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISPRS International Journal of Geo-Information\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.3390/ijgi13090300\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISPRS International Journal of Geo-Information","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.3390/ijgi13090300","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
在地图概括中选择道路网络一直是一项艰巨的挑战,推动着研究向智能模型的应用方向发展。尽管之前做出了很多努力,但这些研究的准确性和连通性仍有待提高,尤其是在处理样本量相似的道路类型时。为了解决这些不足,我们引入了一种用于道路选择的异构图注意力网络(HAN),在该网络中,最初利用特征掩蔽方法来评估道路特征的重要性。在 HAN 框架内,我们将注意力集中在最相关的特征上,并引入了两条元路径:一条是在一阶邻域内聚合相同道路类型的特征,强调局部连通性;另一条是将这种聚合扩展到二阶邻域,捕捉更广泛的空间背景。为了进行综合评估,我们使用了一套同时考虑道路网络定量和定性方面的指标。在样本量相近的道路类型上,HAN 模型在传导型和归纳型任务中的表现都优于其他模型。其准确率(ACC)分别高出 1.62% 和 0.67%,F1 分数分别高出 1.43% 和 0.81%。此外,它还增强了所选网络的整体连通性。总之,我们基于 HAN 的方法为道路网络选择提供了一种先进的解决方案,在准确性和连通性保护方面超越了以往的方法。
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 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.
期刊介绍:
ISPRS International Journal of Geo-Information (ISSN 2220-9964) provides an advanced forum for the science and technology of geographic information. ISPRS International Journal of Geo-Information publishes regular research papers, reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
The 2018 IJGI Outstanding Reviewer Award has been launched! This award acknowledge those who have generously dedicated their time to review manuscripts submitted to IJGI. See full details at http://www.mdpi.com/journal/ijgi/awards.