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Quality Analysis of Open Street Map Data : Volume Xl-2/W1, Issue 1 (13/05/2013)

By Wang, M.

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Book Id: WPLBN0004014657
Format Type: PDF Article :
File Size: Pages 4
Reproduction Date: 2015

Title: Quality Analysis of Open Street Map Data : Volume Xl-2/W1, Issue 1 (13/05/2013)  
Author: Wang, M.
Volume: Vol. XL-2/W1, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus Publications
Publication Date:
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

Wang, M., Hu, Q., Li, Q., & Zhou, M. (2013). Quality Analysis of Open Street Map Data : Volume Xl-2/W1, Issue 1 (13/05/2013). Retrieved from

Description: School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. Crowd sourcing geographic data is an opensource geographic data which is contributed by lots of non-professionals and provided to the public. The typical crowd sourcing geographic data contains GPS track data like OpenStreetMap, collaborative map data like Wikimapia, social websites like Twitter and Facebook, POI signed by Jiepang user and so on. These data will provide canonical geographic information for pubic after treatment. As compared with conventional geographic data collection and update method, the crowd sourcing geographic data from the non-professional has characteristics or advantages of large data volume, high currency, abundance information and low cost and becomes a research hotspot of international geographic information science in the recent years. Large volume crowd sourcing geographic data with high currency provides a new solution for geospatial database updating while it need to solve the quality problem of crowd sourcing geographic data obtained from the non-professionals. In this paper, a quality analysis model for OpenStreetMap crowd sourcing geographic data is proposed. Firstly, a quality analysis framework is designed based on data characteristic analysis of OSM data. Secondly, a quality assessment model for OSM data by three different quality elements: completeness, thematic accuracy and positional accuracy is presented. Finally, take the OSM data of Wuhan for instance, the paper analyses and assesses the quality of OSM data with 2011 version of navigation map for reference. The result shows that the high-level roads and urban traffic network of OSM data has a high positional accuracy and completeness so that these OSM data can be used for updating of urban road network database.



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