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Geo-Foundation Models: Reality, Gaps and Opportunities

Authors:

Shaowen WangAuthors Info & Claims

SIGSPATIAL '23: Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems

November 2023

Article No.: 66, Pages 1 - 4

https://doi.org/10.1145/3589132.3625616

Published: 22 December 2023 Publication History

Abstract

With the recent rapid advances of revolutionary AI models such as ChatGPT, foundation models have become a main topic for the discussion of future AI. Despite the excitement, the success is still limited to specific types of tasks. Particularly, ChatGPT and similar foundation models have unique characteristics that are difficult to replicate for most geospatial tasks. This paper envisions several major challenges and opportunities in the creation of geospatial foundation (geo-foundation) models, as well as potential future adoption scenarios. We also expect that a major success story is necessary for geo-foundation models to take off in the long term.

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Cited By

Mai GHuang WSun JSong SMishra DLiu NGao SLiu TCong GHu YCundy CLi ZZhu RLao N(2024)On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper)ACM Transactions on Spatial Algorithms and Systems10.1145/365307010:2(1-46)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3653070
Zhang YWang ZHe ZLi JMai GLin JWei CYu W(2024)BB-GeoGPT: A framework for learning a large language model for geographic information scienceInformation Processing & Management10.1016/j.ipm.2024.10380861:5(103808)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103808

Index Terms

Geo-Foundation Models: Reality, Gaps and Opportunities
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Conferences

SIGSPATIAL '23: Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems

November 2023

686 pages

ISBN:9798400701689

DOI:10.1145/3589132

Copyright © 2023 Owner/Author(s).

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 December 2023

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SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems

November 13 - 16, 2023

Hamburg, Germany

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Cited By

Mai GHuang WSun JSong SMishra DLiu NGao SLiu TCong GHu YCundy CLi ZZhu RLao N(2024)On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper)ACM Transactions on Spatial Algorithms and Systems10.1145/365307010:2(1-46)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3653070
Zhang YWang ZHe ZLi JMai GLin JWei CYu W(2024)BB-GeoGPT: A framework for learning a large language model for geographic information scienceInformation Processing & Management10.1016/j.ipm.2024.10380861:5(103808)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103808

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