Recognizing the country where a picture has been taken has many potential
applications, such as identification of fake news and prevention of
disinformation campaigns. Previous works focused on the estimation of the
geo-coordinates where a picture has been taken. Yet, recognizing in which
country an image was taken could be more critical, from a semantic and forensic
point of view, than estimating its spatial coordinates. In the above framework,
this paper provides two contributions. First, we introduce the VIPPGeo dataset,
containing 3.8 million geo-tagged images. Secondly, we used the dataset to
train a model casting the country recognition problem as a classification
problem. The experiments show that our model provides better results than the
current state of the art. Notably, we found that asking the network to identify
the country provides better results than estimating the geo-coordinates and
then tracing them back to the country where the picture was taken.

本文利用 3.8 万张包含地理位置信息的图像构建数据集 VIPPGeo，并利用该数据集进行国家识别问题的分类模型训练与实验，发现比直接基于地理坐标估计图片拍摄地并追溯国家更为有效，结果优于现有研究。