Spatial Line Entity Matching Technology for Spatial Association of Multi-source Vector Data
Jingli Jiang, Junfeng Xu, Yilan Lou · 2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) · 2022
Using geometric matching technology to establish the mapping association relationship between multi-source data is currently a research hotspot in the field of spatial association. The present study contributes to this field by proposing a method for matching spatial entities of multi-source vector data. The mapping relationship between spatial entities of multi-source data is obtained by geometric matching between line entities. Standard navigation data and OSM data of Shenzhen province were employed to validate the method. The result of the experiment shows that the selection of threshold significantly influences matching accuracy. When the threshold is selected properly, the proposed algorithm can match line entities effectively, thus establishing the association relationship between spatial entities of multi-source vector data. Moreover, the precision and accuracy are notable.