  Online social networks contain a constantly increasing amount of images -
most of them focusing on people. Due to cultural and climate factors, fashion
trends and physical appearance of individuals differ from city to city. In this
paper we investigate to what extent such cues can be exploited in order to
infer the geographic location, i.e. the city, where a picture was taken. We
conduct a user study, as well as an evaluation of automatic methods based on
convolutional neural networks. Experiments on the Fashion 144k and a
Pinterest-based dataset show that the automatic methods succeed at this task to
a reasonable extent. As a matter of fact, our empirical results suggest that
automatic methods can surpass human performance by a large margin. Further
inspection of the trained models shows that human-centered characteristics,
like clothing style, physical features, and accessories, are informative for
the task at hand. Moreover, it reveals that also contextual features, e.g. wall
type, natural environment, etc., are taken into account by the automatic
methods.
