Flickr Africa: Examining Geo-Diversity inLarge-Scale, Human-Centric Visual Data
Keziah Naggita1; Julienne LaChance2; Alice Xiang2
1Toyota Technological Institute at Chicago, 2Sony AI America
Introduction

Geotagged Images and RWI Distribution

Several popular large-scale computer vision datasets:, e.g.,
ImageNet and COCO, are either fully or partly scraped fromFlickr. Thus, biases inherent to Flickr influence the performanceof models for visual tasks as diverse as object classification,pose estimation, instance segmentation, image captioning,and beyond.

Our Findings
There are very few geotagged images from Africa, mostfrom middle RWI regions, and drastically fewer than
European nations of comparable size e.g., Switzerland has
18x as many geotagged images as Sierra Leone.
The #geotagged images positively increased withpopulation size (correlation: 0.412 & 0.538), internet
usage (0.474 & 0.385), and GDP (0.599 & 0.748) . Official
language was not found to have a meaningful correlation
to the #geotagged images.

We, therefore, investigated the limitations of standard Flickr
image data collection methods in low- and middle-incomecountries.
We analyzed human-centric image geo-diversity on a massive
scale using geotagged Flickr images associated with each
African nation.

The most frequent tags were the name of the place where
the image was taken, including “Africa”. The least
frequent tags were usually those in foreign languages ortags containing typos or consisting of multiple
concatenated words.
Most images were licensed as “All Rights Reserved”(80.46%, 81.99%), indicating the Flickr default setting
when images are uploaded.

We report African geotagged image distribution according tofine-grained intra-national wealth estimates. We compare thequantity and content of available data in Africa to populationmatched nations in Europe.
Our findings also show an “othering” phenomenon as
evidenced by a substantial number of images from Africa
being taken by non-local photographers.

For the geotagged images, we randomly selected for thecrowdsourcing task, African images were far more likely to
be taken by foreigners whereas the opposite trend wasobserved for high-GDP European nations. In addition, ingeneral, European and African images did not containpeople and were captured in outdoor, public, and natural
settings. A majority were real photographs (as opposed
to synthetically-generated images, or pictures of pictures)and did not contain content considered to be offensive byannotators.

Data Collection
Using the FlickrAPI, we scraped geotagged images andassociated metadata from Flickr between dates 2004-02-10
and 2022-02-10 (18 years) by querying by country name andthe country name + “people” for all 54 countries in Africa and4 European countries.
We utilize the normalized by nation relative wealth index datacollected from Low- and Middle-Income Countries byFacebook’s Data for Good project. Using k-nearest neighbour,
we computed the nearest RWI-labeled geographic location ofeach image and excluded geotagged images beyond 300km
to the nearest RWI-labelled location.

Conclusion
Our findings exposed new limitations of current largescale image data collection methodologies and uniquedata challenges to Africa, including the lack of data crucial
to specific domains (e.g., representative household object
data since few images are within indoor/private scenes).

Acknowledgement
We wish to thank Jerone Andrews and Dora Zhao for their expertise and assistance with our work pertaining tocrowdsourcing. We also express gratitude to the whole SONY AI Ethics team for great discussions and informative feedbackon this research.
Reach out to us for feedback: knaggita@ttic.edu, julienne.lachance@sony.com

Looking forward, we encourage new scholarship centeringnovel methods for sourcing geodiverse datasets andmeasuring new forms of geodiversity specific to Africa,such as analyses of tribal diversity as opposed to the morecommonly studied diversity by race/ethnicity.
We openly provide our large-scale dataset to enable
future researchers to utilize and augment Flickr Africa formodel evaluations across a wide domain of computer
vision tasks.