Exploring user image tags for geo-location inference

Geotagging has become a recent phenomenon that allows users to visualize and manage photo collections in many new and interesting ways. Unfortunately, manual geotagging of a large collection of pictures on the globe is still a time-consuming and laborious task even though geotagging devices are gradually being adopted. At the same time, users often provide tag annotations, which may contain useful geographic cues, for their pictures. In this paper, we explore using annotations for inferring the location of images. Using a collection of over a million geotagged pictures, we build location probability maps for tag annotations over the entire globe. These maps reflect the collective picture-taking and tagging behaviors of thousands of users from all over the world. We study the geographic entropy and frequency of user tags as geo-inference features and investigate the usefulness of using these features for selecting geographically meaningful annotations in a probabilistic framework. We show that the geo-distribution of a tag relates to the semantic meaning of the tag itself and we can effectively determine which tags are cities or nations by examining the tag maps themselves. Furthermore, user annotations alone can be used to infer the location of pictures with good accuracy.