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Studies have analysed the quality of volunteered geographic information (VGI) datasets, assessing the positional accuracy offeatures and the completeness of specific attributes. While it has been shown that VGI can, in some context, reach a high positionalaccuracy, these works have also highlighted a large spatial heterogeneity in positional accuracy, completeness but also with regardsto the semantics of the objects. Such high semantic heterogeneity of VGI datasets becomes a significant obstacle to a number ofpossible uses that could be made of the data.  This paper proposes an approach for both improving the semantic quality and reducing the semantic heterogeneity of VGI dat asets.The improvement of the semantic quality is achieved by automatically suggesting attributes to contributors during the editingprocess. The reduction of semantic heterogeneity is achieved by automatically notifying contributors when two attributes are toosimilar or too dissimilar. The approach was implemented into a plugin for OpenStreetMap and different examples illustrate howthis plugin can be used to improve the quality of VGI data

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

IMPROVING VOLUNTEERED GEOGRAPHIC DATA QUALITY USING SEMANTIC SIMILARITY MEASUREMENTS