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On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper)
Gengchen Mai, Weiming Huang, Jin Sun, Suhang Song, Deepak R. Mishra, Ninghao Liu, Song Gao, Tianming Liu et al.. ACM Transactions on Spatial Algorithms and Systems, 2024-03-20
Topic: Risk, prevention and care in population studies
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 not 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 domains, 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, the task-agnostic large learning models (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 FMs still underperform task-specific models. Based on these observations, we propose that one of the major challenges of developing an FM for GeoAI is to address the multimodal nature of geospatial tasks. After discussing the distinct challenges of each geospatial data modality, we suggest the possibility of a multimodal FM that can reason over various types of geospatial data through geospatial alignments. We conclude this article by discussing the unique risks and challenges to developing such a model for GeoAI.
Key findings
- 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, the task-agnostic large learning models (LLMs) can outperform task-specific fully supervised models in a zero-shot or few-shot learning setting.
- We conclude this article by discussing the unique risks and challenges to developing such a model for GeoAI.
Why it matters
This vision paper tested existing foundation models on geospatial tasks, including US state-level and county-level dementia time series forecasting, where large language models outperformed task-specific models in zero-shot or few-shot settings. It is a methods paper about geospatial artificial intelligence and does not address treating, curing, or preventing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (ACM Transactions on Spatial Algorithms and Systems) |
| Journal's citation record | 0 / 4 | ACM Transactions on Spatial Algorithms and Systems: journal h-index 28 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 128 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 32.0 citations a year over 4 year(s) since 2024 |
| Impact for its field and year | 4 / 6 | Cited 19.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 3 / 4 | Cited 128 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Ni Lao: h-index 27 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Stanford University: institution h-index 2,684 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 8 institution(s) in 3 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 48 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1145/3653070
- Original source →
- Free full text: ACM Transactions on Spatial Algorithms and Systems →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Mai, G., Huang, W., Sun, J., Song, S., Mishra, D. R., Liu, N., et al. (2024). On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper). ACM Transactions on Spatial Algorithms and Systems. https://doi.org/10.1145/3653070
Vancouver
Mai G, Huang W, Sun J, Song S, Mishra DR, Liu N, et al. On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper). ACM Transactions on Spatial Algorithms and Systems. 2024. doi:10.1145/3653070.
BibTeX
@article{mai2024-8a9ce09a,
author = {Mai, Gengchen and Huang, Weiming and Sun, Jin and Song, Suhang and Mishra, Deepak R. and Liu, Ninghao and Gao, Song and Liu, Tianming and others},
title = {{On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper)}},
journal = {ACM Transactions on Spatial Algorithms and Systems},
year = {2024},
doi = {10.1145/3653070},
url = {https://doi.org/10.1145/3653070}
}
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