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  • 1.
    Kashian, Alireza
    et al.
    Dept. of Infrastructure Engineering, University of Melbourne, Parkville, Australia.
    Rajabifard, Abbas
    Dept. of Infrastructure Engineering, University of Melbourne, Parkville, Australia.
    Chen, Yiqun
    Dept. of Infrastructure Engineering, University of Melbourne, Parkville, Australia.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    OSM POI Analyzer: A Platform for Assessing Position of POIs in OpenStreetMap2017In: ISPRS Geospatial Week 2017 / [ed] D. Li, J. Gong, B. Yang, H. Wu, L. Wu, Z. Gui, X. Cheng, H. Wu, S. Li, R. Lindenbergh, J. Boehm, M. Rutzinger, W. Yao, M. Weinmann, Z. Kang, K. Khoshelham, M. Peter, L. Díaz-Vilariño, W. Shi, B. Lu, H. Abdulmuttalib, M. R. Delavar, T. Balz, B. Osmanoglu, F. Rocca, U. Sörgel, J. Zhang, P. Li, S. Du, L. Zhao, X. Lin, K. Qin, C. Kang, X. Li, C. Chen, R. Li, G. Qiao, H. Wu, and C. Heipke, 2017, Vol. XLII-2/W7, p. 497-504Conference paper (Refereed)
    Abstract [en]

     In recent years, more and increased participation in Volunteered Geographical Information (VGI) projects provides enough data coverage for most places around the world for ordinary mapping and navigation purposes, however, the positional credibility of contributed data becomes more and more important to bring a long-term trust in VGI data. Today, it is hard to draw a definite traditional boundary between the authoritative map producers and the public map consumers and we observe that more and more volunteers are joining crowdsourcing activities for collecting geodata, which might result in higher rates of man-made mistakes in open map projects such as OpenStreetMap. While there are some methods for monitoring the accuracy and consistency of the created data, there is still a lack of advanced systems to automatically discover misplaced objects on the map. One feature type which is contributed daily to OSM is Point of Interest (POI). In order to understand how likely it is that a newly added POI represents a genuine real-world feature scientific means to calculate a probability of such a POI existing at that specific position is needed. This paper reports on a new analytic tool which dives into OSM data and finds co-existence patterns between one specific POI and its surrounding objects such as roads, parks and buildings. The platform uses a distance-based classification technique to find relationships among objects and tries to identify the high-frequency association patterns among each category of objects. Using such method, for each newly added POI, a probabilistic score would be generated, and the low scored POIs can be highlighted for editors for a manual check. The same scoring method can be used for existing registered POIs to check if they are located correctly. For a sample study, this paper reports on the evaluation of 800 pre-registered ATMs in Paris with associated scores to understand how outliers and fake entries could be detected automatically.

  • 2.
    Kashian, Alireza
    et al.
    University of Melbourne, Australia.
    Rajabifard, Abbas
    University of Melbourne, Australia.
    Richter, Kai-Florian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Chen, Yiqun
    University of Melbourne, Australia.
    Automatic analysis of positional plausibility for points of interest in OpenStreetMap using coexistence patterns2019In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 33, no 7, p. 1420-1443Article in journal (Refereed)
    Abstract [en]

    In the past decade, Volunteered Geographic Information (VGI) has emerged as a new source of geographic information, making it a cheap and universal competitor to existing authoritative data sources. The growing popularity of VGI platforms, such as OpenStreetMap (OSM), would trigger malicious activities such as vandalism or spam. Similarly, wrong entries by unexperienced contributors adds to the complexities and directly impact the reliability of such databases. While there are some existing methods and tools for monitoring OSM data quality, there is still a lack of advanced mechanisms for automatic validation. This paper presents a new recommender tool which evaluates the positional plausibility of incoming POI registrations in OSM by generating near real-time validation scores. Similar to machine learning techniques, the tool discovers, stores and reapplies binary distance-based coexistence patterns between one specific POI and its surrounding objects. To clarify the idea, basic concepts about analysing coexistence patterns including design methodology and algorithms are covered in this context. Furthermore, the results of two case studies are presented to demonstrate the analytical power and reliability of the proposed technique. The encouraging results of this new recommendation tool elevates the need for developing reliable quality assurance systems in OSM and other VGI projects.

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