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

被引:7
|
作者
Wang, Siqin [1 ,2 ,3 ]
Hu, Tao [4 ]
Xiao, Huang [5 ]
Li, Yun [6 ]
Zhang, Ce [7 ]
Ning, Huan [8 ]
Zhu, Rui [7 ]
Li, Zhenlong [8 ]
Ye, Xinyue [9 ,10 ]
机构
[1] Univ Southern Calif, Spatial Sci Inst, Los Angeles, CA USA
[2] Univ Queensland, Sch Environm, Brisbane, Qld, Australia
[3] Royal Melbourne Inst Technol RMIT, Sch Sci, Melbourne, Australia
[4] Oklahoma State Univ, Dept Geog, Stillwater, OK 74077 USA
[5] Emory Univ, Dept Environm Sci, Atlanta, GA USA
[6] Emory Univ, Dept Comp Sci, Atlanta, GA USA
[7] Univ Bristol, Sch Geog Sci, Bristol, England
[8] Penn State Univ, Dept Geog, Geoinformat & Big Data Res Lab, University Pk, PA USA
[9] Texas A&M Univ, Dept Landscape Architecture & Urban Planning, College Stn, TX USA
[10] Texas A&M Univ, Dept Comp Sci & Engn, College Stn, TX USA
关键词
GPT; generative AI (GAI); large language models (LLMs); geospatial science; GIS; WATER-LEVEL; INFORMATION; IMAGES;
D O I
10.1080/17538947.2024.2353122
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
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.
引用
收藏
页数:21
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