# GeoDiv: Framework For Measuring Geographical Diversity In Text-To-Image Models

arXiv: https://arxiv.org/abs/2602.22120 · HTML: https://mlsanity.com/paper/2602.22120/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2026.

## Built on by (most cited first, 2 of 2)

- [From Codebooks to VLMs: Evaluating Automated Visual Discourse Analysis for Climate Change on Social Media](https://mlsanity.com/paper/2604.21786/) (2026)
- [Failure or Drift? Evaluating Monocular SLAM under Synthetic and Real-World Corruptions](https://mlsanity.com/paper/2608.30690/) (2026)

## Abstract (arXiv)

Text-to-image (T2I) models are rapidly gaining popularity, yet their outputs often lack geographical diversity, reinforce stereotypes, and misrepresent regions. Given their broad reach, it is critical to rigorously evaluate how these models portray the world. Existing diversity metrics either rely on curated datasets or focus on surface-level visual similarity, limiting interpretability. We introduce GeoDiv, a framework leveraging large language and vision-language models to assess geographical diversity along two complementary axes: the Socio-Economic Visual Index (SEVI), capturing economic and condition-related cues, and the Visual Diversity Index (VDI), measuring variation in primary entities and backgrounds. Applied to images generated by models such as Stable Diffusion and FLUX.1-dev across 10 entities and 16 countries, GeoDiv reveals a consistent lack of diversity and identifies fine-grained attributes where models default to biased portrayals. Strikingly, depictions of countries like India, Nigeria, and Colombia are disproportionately impoverished and worn, reflecting underlying socio-economic biases. These results highlight the need for greater geographical nuance in generative models. GeoDiv provides the first systematic, interpretable framework for measuring such biases, marking a step toward fairer and more inclusive generative systems. Project page: https://abhipsabasu.github.io/geodiv