  The application of machine learning (ML) in a range of geospatial tasks is
increasingly common but often relies on globally available covariates such as
satellite imagery that can either be expensive or lack predictive power. Here
we explore the question of whether the vast amounts of knowledge found in
Internet language corpora, now compressed within large language models (LLMs),
can be leveraged for geospatial prediction tasks. We first demonstrate that
LLMs embed remarkable spatial information about locations, but naively querying
LLMs using geographic coordinates alone is ineffective in predicting key
indicators like population density. We then present GeoLLM, a novel method that
can effectively extract geospatial knowledge from LLMs with auxiliary map data
from OpenStreetMap. We demonstrate the utility of our approach across multiple
tasks of central interest to the international community, including the
measurement of population density and economic livelihoods. Across these tasks,
our method demonstrates a 70% improvement in performance (measured using
Pearson's $r^2$) relative to baselines that use nearest neighbors or use
information directly from the prompt, and performance equal to or exceeding
satellite-based benchmarks in the literature. With GeoLLM, we observe that
GPT-3.5 outperforms Llama 2 and RoBERTa by 19% and 51% respectively, suggesting
that the performance of our method scales well with the size of the model and
its pretraining dataset. Our experiments reveal that LLMs are remarkably
sample-efficient, rich in geospatial information, and robust across the globe.
Crucially, GeoLLM shows promise in mitigating the limitations of existing
geospatial covariates and complementing them well. Code is available on the
project website: https://rohinmanvi.github.io/GeoLLM
