LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing

Open Geosciences

Gong Xianyong¹  Xing Ruixing¹  Wu Fang¹  Du Jiawei¹  Liu Chengyi¹ 

[1] Institute of Surveying and Mapping, Information Engineering University, Kexue Road 62, Zhengzhou 450001, China;

关键词: cartographic generalization;    spatial data mining;    spatial cluster;    lane-level road cluster;    distance measurement;   

DOI  :  10.1515/geo-2020-0271

来源: DOAJ

【 摘 要 】

Lane-level road cluster is a most representative phenomenon in road networks and is vital to spatial data mining, cartographic generalization, and data integration. In this article, a lane-level road cluster recognition method was proposed. First, the conception of lane-level road cluster and our motivation were addressed and the spatial characteristics were given. Second, a region growing cluster algorithm was defined to recognize lane-level road clusters, where constraints including distance and orientation were used. A novel moving distance (MD) metric was proposed to measure the distance of two lines, which can effectively handle the non-uniformly distributed vertexes, heterogeneous length, inharmonious spatial alignment, and complex shape. Experiments demonstrated that the proposed method can effectively recognize lane-level road clusters with the agreement to human spatial cognition.

【 授权许可】

Unknown