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Mining the co-existence of POIs in OpenStreetMap for faulty entry detection


Kashian, Alireza; Richter, Kai-Florian; Rajabifard, Abbas; Chen, Yiqun (2016). Mining the co-existence of POIs in OpenStreetMap for faulty entry detection. In: Both, Alan; Duckham, Matt; Kealy, Allison. Proceedings of the 3rd annual conference of Research@Locate, the academic research stream at Locate 2016. Melbourne: CEUR-WS, online.

Abstract

In recent years, more and more volunteers join crowdsourcing activities for collecting geodata which in turn might result in higher rates of man-made mistakes in open geo-spatial databases such as OpenStreetMap (OSM). 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. In order to understand how likely it is that a newly added POI represents a genuine real-world feature, some means to calculate a probability of a POI existing at that specific position is needed. This paper reports on work in progress on a platform for analysing POI objects in the OSM database in order to find patterns of co-existence among features in close distance to each other. These patterns will improve current tracking and verifying systems and, thus, enhance positional accuracy of registered POIs in OSM.

Abstract

In recent years, more and more volunteers join crowdsourcing activities for collecting geodata which in turn might result in higher rates of man-made mistakes in open geo-spatial databases such as OpenStreetMap (OSM). 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. In order to understand how likely it is that a newly added POI represents a genuine real-world feature, some means to calculate a probability of a POI existing at that specific position is needed. This paper reports on work in progress on a platform for analysing POI objects in the OSM database in order to find patterns of co-existence among features in close distance to each other. These patterns will improve current tracking and verifying systems and, thus, enhance positional accuracy of registered POIs in OSM.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2016
Deposited On:14 Apr 2016 17:04
Last Modified:14 Apr 2016 17:04
Publisher:CEUR-WS
Series Name:CEUR Workshop Proceedings
Number:1570
ISSN:1613-0073
Additional Information:Proceedings of the 3rd annual conference of Research@Locate, the academic research stream at Locate 2016 Melbourne, Australia, April 12 - 14, 2016
Official URL:http://ceur-ws.org/Vol-1570/paper20.pdf
Related URLs:http://ceur-ws.org/Vol-1570/

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