GPT, large language models (LLMs) and generative artificial intelligence (GAI) models in geospatial science: a systematic review

Siqin Wang, Tao Hu*, Xiao Huang, Yun Li, Ce Zhang, Huan Ning, Rui Zhu, Zhenlong Li, Xinyue Ye

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

17 Citations (Scopus)
154 Downloads (Pure)

Abstract

The launch of large language models (LLMs) like ChatGPT in late 2022 and the anticipated arrival of future GPT-x iterations have marked the beginning of the generative artificial intelligence (GAI) era. We conducted a systematic review of how to integrate LLMs including GPT and other GAI models into geospatial science, based on 293 papers obtained from four databases of academic publications – Web of Science (WoS), Scopus, SSRN and arXiv – 26 papers were eventually included for analysis. We statistically outlined the share of domains where LLMs and other GAI models, the type of data that have been used for these models, and the modelling tasks and roles that they play. We also pointed out the challenges and future directions for the next research agenda – along with which we could better position ourselves in the mainstream of science and the cutting-edge research paradigm as others leverage insights from the growing data deluge.
Original languageEnglish
Article number2353122
Number of pages21
JournalInternational Journal of Digital Earth
Volume17
Issue number1
DOIs
Publication statusPublished - 20 May 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Keywords

  • GPT
  • generative AI (GAI)
  • large language models (LLMs)
  • geospatial science
  • GIS

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