  Generative AI based on foundation models provides a first glimpse into the
world represented by machines trained on vast amounts of multimodal data
ingested by these models during training. If we consider the resulting models
as knowledge bases in their own right, this may open up new avenues for
understanding places through the lens of machines. In this work, we adopt this
thinking and select GPT-4, a state-of-the-art representative in the family of
multimodal large language models, to study its geographic diversity regarding
how well geographic features are represented. Using DBpedia abstracts as a
ground-truth corpus for probing, our natural language--based geo-guessing
experiment shows that GPT-4 may currently encode insufficient knowledge about
several geographic feature types on a global level. On a local level, we
observe not only this insufficiency but also inter-regional disparities in
GPT-4's geo-guessing performance on UNESCO World Heritage Sites that carry
significance to both local and global populations, and the inter-regional
disparities may become smaller as the geographic scale increases. Morever,
whether assessing the geo-guessing performance on a global or local level, we
find inter-model disparities in GPT-4's geo-guessing performance when comparing
its unimodal and multimodal variants. We hope this work can initiate a
discussion on geographic diversity as an ethical principle within the GIScience
community in the face of global socio-technical challenges.
