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.

通过对非洲国家的地理标记的 Flickr 图像进行大规模分析，研究表明标准的互联网数据收集方法在低收入和中等收入国家存在局限性，需要进一步努力来捕捉非洲人和他们环境的图像数据，以改善计算机视觉模型在全球范围内的适用性。