Urban Land-Use Classification From Photographs

Land-use (LU) classification of urban areas is conventionally achieved via field survey or remote sensing technologies, which is labor-intensive and time-consuming. With the wide development of social networks such as microblog and ubiquitous network access, images are captured by residents and tourists. In this letter, we propose a method for an automatic urban LU classification using geotagged images from public venues. Our method identifies the LU type depicted in those images that are extrapolated to the local regions bounded by street blocks. Experiments were conducted with geotagged photographs and Open Street Map of an urban area in London, U.K. It was demonstrated that the proposed method achieved overall 76.5% accuracy across five LU types. More importantly, our method demonstrated a greater performance in dealing with a mixture of LU types.