Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data
This work highlights critical data representation issues for improving computer vision models globally, particularly for low- and middle-income countries, but is incremental as it builds on existing bias research.
The study analyzed geotagged Flickr images in Africa to assess geo-diversity biases in human-centric visual data, revealing substantial data gaps and an 'othering' phenomenon with many images taken by non-local photographers.
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.