  Large pre-trained models, also known as foundation models (FMs), are trained
in a task-agnostic manner on large-scale data and can be adapted to a wide
range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning.
Despite their successes in language and vision tasks, we have yet seen an
attempt to develop foundation models for geospatial artificial intelligence
(GeoAI). In this work, we explore the promises and challenges of developing
multimodal foundation models for GeoAI. We first investigate the potential of
many existing FMs by testing their performances on seven tasks across multiple
geospatial subdomains including Geospatial Semantics, Health Geography, Urban
Geography, and Remote Sensing. Our results indicate that on several geospatial
tasks that only involve text modality such as toponym recognition, location
description recognition, and US state-level/county-level dementia time series
forecasting, these task-agnostic LLMs can outperform task-specific
fully-supervised models in a zero-shot or few-shot learning setting. However,
on other geospatial tasks, especially tasks that involve multiple data
modalities (e.g., POI-based urban function classification, street view
image-based urban noise intensity classification, and remote sensing image
scene classification), existing foundation models still underperform
task-specific models. Based on these observations, we propose that one of the
major challenges of developing a FM for GeoAI is to address the multimodality
nature of geospatial tasks. After discussing the distinct challenges of each
geospatial data modality, we suggest the possibility of a multimodal foundation
model which can reason over various types of geospatial data through geospatial
alignments. We conclude this paper by discussing the unique risks and
challenges to develop such a model for GeoAI.
