Abstract. The amount of crowdsourced geospatial content on the Web is constantly increasing, providing a wealth of information for a variety of location-based services and applications. This content can be analyzed to discover interesting locations in large urban environments which people choose for different purposes, such as for entertainment, shopping, business or culture. In this paper, we focus on the problem of identifying and describing Streets of Interest. Given the road network in a specified area, and a collection of geolocated Points of Interest and photos in this area, our goal is to identify the most interesting streets for a specified category or keyword set, and to allow their visual exploration by selecting a small and spatio-textually diverse set of relevant photos. We formally define the problem and we present efficient algorithms, based on spatio-textual indices and filter and refinement strategies. The proposed methods are evaluated experimentally regarding their effectiveness and efficiency, using three real-world datasets containing road networks, POIs and photos collected from several Web sources.