A probabilistic relaxation approach for matching road networks

Geospatial data matching is an important prerequisite for data integration, change detection and data updating. At present, crowdsourcing geospatial data are attracting considerable attention with its significant potential for timely and cost-effective updating of geospatial data and Geographical Information Science (GIS) applications. To integrate the available and up-to-date information of multi-source geospatial data, this article proposes a heuristic probabilistic relaxation road network matching method. The proposed method starts with an initial probabilistic matrix according to the dissimilarities in the shapes and then integrates the relative compatibility coefficient of neighbouring candidate pairs to iteratively update the initial probabilistic matrix until the probabilistic matrix is globally consistent. Finally, the initial 1:1 matching pairs are selected on the basis of probabilities that are calculated and refined on the basis of the structural similarity of the selected matching pairs. A process of matching is then implemented to find M:N matching pairs. Matching between OpenStreetMap network data and professional road network data shows that our method is independent of matching direction, successfully matches 1:0 (Null), 1:1 and M:N pairs, and achieves a robust matching precision of above 95%.