@inproceedings{b96d51031c8141c7ba1b6b50a6debed2,
title = "Geo-Foundation Models: Reality, Gaps and Opportunities (Vision Paper)",
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.",
keywords = "AI, foundation models, GeoAI, geospatial data",
author = "Yiqun Xie and Zhaonan Wang and Gengchen Mai and Yanhua Li and Xiaowei Jia and Song Gao and Shaowen Wang",
note = "Publisher Copyright: {\textcopyright} 2023 Owner/Author(s).; 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023 ; Conference date: 13-11-2023 Through 16-11-2023",
year = "2023",
month = nov,
day = "13",
doi = "10.1145/3589132.3625616",
language = "English (US)",
series = "GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems",
publisher = "Association for Computing Machinery",
editor = "Damiani, {Maria Luisa} and Matthias Renz and Ahmed Eldawy and Peer Kroger and Nascimento, {Mario A.}",
booktitle = "31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023",
address = "United States",
}