Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric
  Visual Data
Keziah Naggita Julienne LaChance Alice Xiang
Biases in large-scale image datasets are known to influence the performance
of computer vision models as a function of geographic context. To investigate
the limitations of standard Internet data collection methods in low- and
middle-income countries, we analyze human-centric image geo-diversity on a
massive scale using geotagged Flickr images associated with each nation in
Africa. We report the quantity and content of available data with comparisons
to population-matched nations in Europe as well as the distribution of data
according to fine-grained intra-national wealth estimates. Temporal analyses
are performed at two-year intervals to expose emerging data trends.
Furthermore, we present findings for an ``othering'' phenomenon as evidenced by
a substantial number of images from Africa being taken by non-local
photographers. The results of our study suggest that further work is required
to capture image data representative of African people and their environments
and, ultimately, to improve the applicability of computer vision models in a
global context.