Affiliations: [a] School of Geography and Planning, Nanning Normal University, Nanning, Guangxi, China | [b] School of Computer and Information, Nanning Normal University, Nanning, Guangxi, China
Correspondence:
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Corresponding author: Kaixian Qin, School of Computer and Information, Nanning Normal University, Nanning, Guangxi 530000, China. E-mail: [email protected].
Abstract: Entity matching is one of the key technologies for geospatial data update and fusion. In response to the shortcomings of most spatial entity matching methods that use local optimisation strategies, a global optimisation matching method for multi-representation buildings using road network constraints is proposed. First, the road network is used for region segmentation to obtain candidate matches. Second, the spatial similarity among the candidate matching objects is calculated and the characteristic similarity weights are determined using the entropy weight method. Third, the matching of building entities is transformed into an allocation problem using integer programming ideas, and the Hungarian algorithm is solved to obtain the optimal matching combination with minimum global variance. Finally, two test areas are selected to validate the proposed method, and the precision, recall, and F-measure values of the experiments are 96.35%, 97.11%, and 96.73% versus 95.96%, 97.03%, and 96.49%, respectively. The matching accuracy is greatly improved compared with the local search strategy.
Keywords: Entity matching, global optimisation, geospatial database, building
