City exploration by use of spatio-temporal analysis and clustering of user contributed photos

We present a technical demonstration of an online city exploration application that helps users identify interesting spots in a city by use of spatio-temporal analysis and clustering of user contributed photos. Our framework analyzes the spatial distribution of large city-centered collections of user contributed photos at different time scales in order to index the most popular spots of a city in a time-aware manner. Subsequently, the photo sets belonging to the same spatiotemporal context are clustered in order to extract representative photos for each spot. The resulting application enables users to obtain flexible summaries of the most important spots in a city given a temporal slice (time of the day, month, season). The demonstration will be based on a photo dataset covering major European cities.