Abstract
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”.
Highlights
At the moment, disinformation has become a real threat in the digital world, on crowdsourcing platforms
Disinformation has become a real threat in the digital world, on crowdsourcing platforms. In this respect, Volunteered Geographic Information (VGI) has not been spared as it is prone to vandalism
We propose a model for such a corpus in the first subsection, and we describe in the following subsections how we instantiated this corpus with synthetic vandalism
Summary
Disinformation has become a real threat in the digital world, on crowdsourcing platforms. In this respect, Volunteered Geographic Information (VGI) has not been spared as it is prone to vandalism. In order to be reusable and trustworthy, crowdsourced spatial data should ensure a certain level of quality. With the aim of providing a minimal level of quality of VGI, one possible solution could be to automatically detect vandalism. In the case of VGI, very few tools were devised to address this issue precisely; current research is not able to automatically detect carto-vandalism [5,9]
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