ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-4, 281-286, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
20 Jul 2012
Y. Zhang2,1, B. Yang2,1, and X. Luan2,1 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University,Wuhan, China, 430079
2Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application, Wuhan University, Wuhan, China, 430079
Keywords: road matching, probabilistic relaxation, data integration, crowdsourcing geospatial data, OpenStreetMap Abstract. Geospatial data matching is an important prerequisite for data integration, change detection, and data updating. Presently, crowdsourcing geospatial data is drawing great attention with its significant potential for geospatial data updating and Location Based Services, etc. To explore the availabilities of crowdsourcing geospatial data, the paper proposes a heuristic probabilistic relaxation road matching method, named PRRM. It starts with an initial probabilistic matrix according to geometric dissimilarities and then integrates the relative compatibility coefficient of neighbouring candidate pairs to update the previous matrix. Finally, the initial 1:1 matching pairs are selected based on probabilities calculated and refined based on the structure similarity of the selected matching pairs, then a matching growing process is implemented to find M: N matching pairs. Two experiments between OpenStreetMap and professional data show that our method achieves good performance in matching crowdsourcing and professional data with non-rigid deviations and inconsistent structures. Moreover, the proposed method is independent on matching direction and could handle 1: 0 (Null), 1: M and M: N matching.
Conference paper (PDF, 861 KB)

Citation: Zhang, Y., Yang, B., and Luan, X.: AUTOMATED MATCHING CROWDSOURCING ROAD NETWORKS USING PROBABILISTIC RELAXATION, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-4, 281-286,, 2012.

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