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.

在这篇论文中，我们探讨了机器学习在地理空间预测任务中的应用，提出了一种名为 GeoLLM 的新方法，利用大型语言模型中的地理空间信息和开放街道地图的辅助数据，有效地提取地理空间知识，用于测量人口密度等中心问题，相较于最近邻和直接使用提示信息的基准方法，在多个任务上展现了 70% 的性能提升，并且与卫星数据的基准结果相当甚至超出，证明了大型语言模型在地理空间任务上的可行性和潜力。

GeoLLM：从大型语言模型中提取地理空间知识

GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Estimating the geographical range of a species from sparse observations is a
challenging and important geospatial prediction problem. Given a set of
locations where a species has been observed, the goal is to build a model to
predict whether the species is present or absent at any location. This problem
has a long history in ecology, but traditional methods struggle to take
advantage of emerging large-scale crowdsourced datasets which can include tens
of millions of records for hundreds of thousands of species. In this work, we
use Spatial Implicit Neural Representations (SINRs) to jointly estimate the
geographical range of 47k species simultaneously. We find that our approach
scales gracefully, making increasingly better predictions as we increase the
number of species and the amount of data per species when training. To make
this problem accessible to machine learning researchers, we provide four new
benchmarks that measure different aspects of species range estimation and
spatial representation learning. Using these benchmarks, we demonstrate that
noisy and biased crowdsourced data can be combined with implicit neural
representations to approximate expert-developed range maps for many species.

本研究利用空间隐式神经表示联合估计 47k 个物种的地理范围，建立了四个新的基准模型，证明嘈杂和有偏差的众包数据可以与隐式神经表示相结合，近似许多物种的专家开发的范围图。