Multi Criteria Estimation Method for Scale of Volunteered Vector Map

Objectives: Scale is crucial when integrating volunteered vector map data (e.g.,OpenStreetMap) into multi-source spatial data fusion systems. Significant scale discrepancies between datasets frequently lead to spatial inconsistencies, severely compromising the quality and applicability of fused map data. This study aims to address this issue by developing an automated scale estimation method for vector maps. Methods: This paper proposes an automatic scale estimation method for vector maps based on the Electre-tri (Elimination and Choice Translating Reality with Triangle Interaction) multi criteria decision model. Firstly, the evaluation parameters of geographic spatial data scale were analyzed, and the mapping relationship between 8 spatial scale evaluation parameters and vector map scale was established; Then, based on the Electre-tri multi criteria decision model, the correlation calculation between the vector map data to be evaluated and the reference sample map data is achieved by dynamically setting three types of thresholds: preference threshold, indifference threshold, and rejection threshold. Finally, the spatial scale estimation of vector map data is completed. Results: Experiments on 10 boundary scales of OpenStreetMap (OSM) data in Beijing demonstrate the method’s effectiveness. Results indicate strong practicality in estimating vector map spatial scales, with high operability of model parameters. Conclusions: The proposed method provides an operable solution for automated scale estimation in volunteered vector maps, effectively supporting multi-source spatial data fusion by reducing scale-induced inconsistencies.