  Moving beyond traditional surveys, combining heterogeneous data sources with
AI-driven inference models brings new opportunities to measure socio-economic
conditions, such as poverty and population, over expansive geographic areas.
The current research presents GeoSEE, a method that can estimate various
socio-economic indicators using a unified pipeline powered by a large language
model (LLM). Presented with a diverse set of information modules, including
those pre-constructed from satellite imagery, GeoSEE selects which modules to
use in estimation, for each indicator and country. This selection is guided by
the LLM's prior socio-geographic knowledge, which functions similarly to the
insights of a domain expert. The system then computes target indicators via
in-context learning after aggregating results from selected modules in the
format of natural language-based texts. Comprehensive evaluation across
countries at various stages of development reveals that our method outperforms
other predictive models in both unsupervised and low-shot contexts. This
reliable performance under data-scarce setting in under-developed or developing
countries, combined with its cost-effectiveness, underscores its potential to
continuously support and monitor the progress of Sustainable Development Goals,
such as poverty alleviation and equitable growth, on a global scale.
