The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 633–638, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-633-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 633–638, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-633-2022
 
02 Jun 2022
02 Jun 2022

GLOBAL ROAD NETWORK DATASET FUSION BASED ON TRACEABILITY MECHANISM

C. Wu1, H. Zhang1, X. Du1, Z. Li2, S. Lin1, and J. Liu1 C. Wu et al.
  • 1National Geomatics Center of China, Beijing, China
  • 2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

Keywords: Data fusion, Road network dataset, Traceability mechanism, Confidence

Abstract. Accurate road network information is the foundation of urban construction, traffic planning and emergency response, and also provides necessary assistance for public travel. How to provide global road network data products with complete elements, rich attributes, and high precision is a problem that must be considered in the reserve of basic geographic information resources.This paper studies the geometric information fusion of multi-source data based on OSM road network data, mainly carries out relevant research work on the identification and matching of homonymous entities, and proposes a method based on traceability mechanism to solve the problem of inconsistent geographical location in different data sets after topology preprocessing of homonymous features, so as to improve the integrity and accuracy of road network data after fusion.

This paper proposes a method based on traceability mechanism, and explores a technical processing flow of global road network data fusion. The test process greatly improves the matching effect of entities with the same name in different data sets, greatly ensures the original topology relationship, and improves the integrity and accuracy of the fused road network data. The data test result shows that this method has certain application value and can basically meet the actual needs of multi-source road network data fusion from a global perspective.