Road network matching based on graph convolutional neural network

doi:10.13474/j.cnki.11-2246.2023.0353

Abstract

Abstract: The current road network matching methods are highly subjective in determining the weighted relations and matching threshold and prone to matching errors. Therefore, a road network matching method combining graph convolutional neural network is proposed. First, four similarity measure factors of length, direction, distance and topology are selected as the feature factors of the road network matching model. Then the roads to be matched are transformed into a dual graph with roads as nodes and road connection relations as edges, thus to match road network through node classification. Finally, the road network matching is realized by building a graph convolutional neural network. The results show that the matching accuracy, recall rate, and F-value of this method are greatly improved compared with the traditional methods, and it can effectively solve the road network matching problem.

Key words: road network matching, graph convolutional neural network, deep learning

CLC Number:

P208

QI Jie, WANG Zhonghui, LI Yiyan. Road network matching based on graph convolutional neural network[J]. Bulletin of Surveying and Mapping, 2023, 0(12): 19-24,44.

References

[1] 郭宁宁. 矢量道路数据的自动匹配与变化检测研究[D]. 南京: 南京师范大学, 2017.
[2] 吴冰娇,王中辉,杨飞. 用于多尺度道路网匹配的语义相似性计算模型[J].测绘科学,2022,47(3):166-173.
[3] 郭小菊,陈俊杰. 基于地理本体的同名实体匹配技术研究[J]. 计算机应用与软件,2015,32(2): 66-68.
[4] YANG Xue,TANG Luliang,STEWART K,et al. Automatic change detection in lane-level road networks using GPS trajectories[J]. International Journal of Geographical Information Science,2018,32(3): 601-621.
[5] 安晓亚,刘平芝,杨云,等. 一种线状要素几何相似性度量方法及其应用[J]. 武汉大学学报(信息科学版),2015,40(9): 1225-1229.
[6] 陈玉敏,龚健雅,史文中. 多尺度道路网的距离匹配算法研究[J]. 测绘学报,2007,36(1): 84-90.
[7] SAALFELD A. Conflation automated map compilation[J]. International Journal of Geographical Information Systems,1988,2(3): 217-228.
[8] 王米琪,艾廷华,晏雄锋,等. 图卷积网络模型识别道路正交网格模式[J]. 武汉大学学报(信息科学版),2020,45(12): 1960-1969.
[9] 张康,郑静,沈婕,等. 图卷积网络在道路网选取中的应用[J]. 测绘科学,2021,46(2): 165-170.
[10] BRUNA J,ZAREMBA W,SZLAM A,et al. Spectral networks and locally connected networks on graphs[C]//Proceedings of 2013 International Conference on Learning Representations.Scottsdale:[s.n.],2013.
[11] DEFFERRARD M,BRESSON X,VANDERGHEYNST P. Convolutional neural networks on graphs with fast localized spectral filtering[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. New York: ACM Press,2016: 3844-3852.
[12] KIPF T N,WELLING M. Semi-supervised classification with graph convolutional networks[C]//Proceedings of 2017 International Commission for Labor Rights(ICLR). Toulon:[s.n.],2017.
[13] 余梦娟. 基于Voronoi图的多尺度道路网匹配方法研究[D]. 南昌: 江西师范大学,2017.
[14] ZHANG M,SHI W,MENG L.A generic matching algorithm for line networks of different resolutions[C]//Proceedings of 2005 Ica Workshop on Generalisation & Multiple Representation A Corua. Spain: [s.n.],2005.
[15] 张猛,吴巧丽,钱海忠. 一种适用于多源道路网自动匹配的通用算法[J]. 测绘科学技术学报,2018,35(1): 82-86.
[16] FAN Hongchao,YANG Bisheng,ZIPF A,et al. A polygon-based approach for matching OpenStreetMap Road networks with regional transit authority data[J]. International Journal of Geographical Information Science,2016,30(4): 748-764.