An Analysis of Human-centered Geolocation
This addresses the challenge of geolocation from images for social network analysis, but it is incremental as it builds on existing methods with new datasets.
The paper tackled the problem of inferring the geographic location (city) of a photo using human-centered cues like fashion and appearance, and found that automatic convolutional neural network methods can achieve reasonable accuracy, surpassing human performance by a large margin.
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.