Towards precise POI localization with social media

Points of interest (POIs) are a core component of geographical databases and of location based services. POI acquisition was performed by domain experts but associated costs and access difficulties in many regions of the world reduce the coverage of manually built geographical databases. With the availability of large geotagged multimedia datasets on the Web, a sustained research effort was dedicated to automatic POI discovery and characterization. However, in spite of its practical importance, POI localization was only marginally addressed. To compute POI coordinates an assumption was made that the more data were available, the more precise the localization will be. Here we shift the focus of the process from data quantity to data quality. Given a set of geotagged Flickr photos associated to a POI, close-up classification is used to trigger a spatial clustering process. To evaluate the newly introduced method against different other localization schemes, we create an accurate ground truth. We show that significant localization error reductions are obtained compared to a coordinate averaging approach and to a X-Means clustering scheme.