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Towards Automatic Vandalism Detection in OpenStreetMap
Geoinformatics Research Group, Department of Geography, University of Heidelberg, Berliner Street 48, D-69120 Heidelberg, Germany
* Author to whom correspondence should be addressed.
Received: 8 October 2012; in revised form: 5 November 2012 / Accepted: 16 November 2012 / Published: 22 November 2012
Abstract: The OpenStreetMap (OSM) project, a well-known source of freely available worldwide geodata collected by volunteers, has experienced a consistent increase in popularity in recent years. One of the main caveats that is closely related to this popularity increase is different types of vandalism that occur in the projects database. Since the applicability and reliability of crowd-sourced geodata, as well as the success of the whole community, are heavily affected by such cases of vandalism, it is essential to counteract those occurrences. The question, however, is: How can the OSM project protect itself against data vandalism? To be able to give a sophisticated answer to this question, different cases of vandalism in the OSM project have been analyzed in detail. Furthermore, the current OSM database and its contributions have been investigated by applying a variety of tests based on other Web 2.0 vandalism detection tools. The results gathered from these prior steps were used to develop a rule-based system for the automated detection of vandalism in OSM. The developed prototype provides useful information about the vandalism types and their impact on the OSM project data.
Keywords: investigation; vandalism; detection; OpenStreetMap; Volunteered Geographic Information (VGI)
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Cite This Article
MDPI and ACS Style
Neis, P.; Goetz, M.; Zipf, A. Towards Automatic Vandalism Detection in OpenStreetMap. ISPRS Int. J. Geo-Inf. 2012, 1, 315-332.
Neis P, Goetz M, Zipf A. Towards Automatic Vandalism Detection in OpenStreetMap. ISPRS International Journal of Geo-Information. 2012; 1(3):315-332.
Neis, Pascal; Goetz, Marcus; Zipf, Alexander. 2012. "Towards Automatic Vandalism Detection in OpenStreetMap." ISPRS Int. J. Geo-Inf. 1, no. 3: 315-332.