Regional biases in image geolocation estimation: a case study with the
  SenseCity Africa dataset
Ximena Salgado Uribe Martí Bosch Jérôme Chenal
Advances in Artificial Intelligence are challenged by the biases rooted in
the datasets used to train the models. In image geolocation estimation, models
are mostly trained using data from specific geographic regions, notably the
Western world, and as a result, they may struggle to comprehend the
complexities of underrepresented regions. To assess this issue, we apply a
state-of-the-art image geolocation estimation model (ISNs) to a crowd-sourced
dataset of geolocated images from the African continent (SCA100), and then
explore the regional and socioeconomic biases underlying the model's
predictions. Our findings show that the ISNs model tends to over-predict image
locations in high-income countries of the Western world, which is consistent
with the geographic distribution of its training data, i.e., the IM2GPS3k
dataset. Accordingly, when compared to the IM2GPS3k benchmark, the accuracy of
the ISNs model notably decreases at all scales. Additionally, we cluster images
of the SCA100 dataset based on how accurately they are predicted by the ISNs
model and show the model's difficulties in correctly predicting the locations
of images in low income regions, especially in Sub-Saharan Africa. Therefore,
our results suggest that using IM2GPS3k as a training set and benchmark for
image geolocation estimation and other computer vision models overlooks its
potential application in the African context.