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Open AccessEditor’s ChoiceArticle

OSMWatchman: Learning How to Detect Vandalized Contributions in OSM Using a Random Forest Classifier

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COSYS-LISIS, Université Gustave Eiffel, IFSTTAR, 77454 Marne-la-Vallée, France
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LASTIG, Université Gustave Eiffel, ENSG, IGN, 94160 Saint-Mande, France
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BDTLN, LIFAT, University of Tours, F-41000 Blois, France
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MODECO, CReSTIC, University of Reims Champagne-Ardenne, 51687 Reims, France
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Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(9), 504; https://doi.org/10.3390/ijgi9090504
Received: 21 June 2020 / Revised: 9 August 2020 / Accepted: 19 August 2020 / Published: 22 August 2020
(This article belongs to the Special Issue Crowdsourced Geographic Information in Citizen Science)
Though Volunteered Geographic Information (VGI) has the advantage of providing free open spatial data, it is prone to vandalism, which may heavily decrease the quality of these data. Therefore, detecting vandalism in VGI may constitute a first way of assessing the data in order to improve their quality. This article explores the ability of supervised machine learning approaches to detect vandalism in OpenStreetMap (OSM) in an automated way. For this purpose, our work includes the construction of a corpus of vandalism data, given that no OSM vandalism corpus is available so far. Then, we investigate the ability of random forest methods to detect vandalism on the created corpus. Experimental results show that random forest classifiers perform well in detecting vandalism in the same geographical regions that were used for training the model and has more issues with vandalism detection in “unfamiliar regions”. View Full-Text
Keywords: vandalism; OpenStreetMap; volunteered geographic information; supervised machine learning; random forest; quality vandalism; OpenStreetMap; volunteered geographic information; supervised machine learning; random forest; quality
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Truong, Q.T.; Touya, G.; Runz, C.D. OSMWatchman: Learning How to Detect Vandalized Contributions in OSM Using a Random Forest Classifier. ISPRS Int. J. Geo-Inf. 2020, 9, 504.

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