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Research Article

Evolution of Generative AI-based Geospatial Technologies (GeoAGI): Deriving Strategic Directions through a Literature Review

생성형 AI 기반 공간정보 기술(GeoAGI)의 진화 - 문헌 고찰을 통한 추진 전략 도출 -

Sung-Jin Choa
,
Kangmin Kimb
,
Taekeon Hwangc
,
Jeon-Young Kangd*
조 성진a
,
김 강민b
,
황 태건c
,
강 전영d*
aAssociate Research Fellow, Marine Policy Research Department, Korea Maritime Institute
bAssistant Professor, Department of Geospatial Big Data,Inha Tech College
cSenior Researcher, Marine Policy Research Department, Korea Maritime Institute
dAssistant Professor, Department of Geography, Kyung Hee University
a한국해양수산개발원 해양연구본부 부연구위원
b인하공업전문대학 공간정보빅데이터학과 조교수
c한국해양수산개발원 해양연구본부 전문연구원
d경희대학교 지리학과 조교수
*Corresponding Author
28 February 2026. pp. 55-68
PDF XML Citation
Abstract

Recent advances in generative artificial intelligence, particularly large language model (LLM), have led to various forms of evolution in the field of GIScience. While conventional GeoAI has primarily focused on conducting specific tasks such as deep learning-based image analysis and spatiotemporal prediction, there is a growing demand for a more general form of spatial intelligence that integrates natural language understanding, multimodal data fusion, and automated analytical workflows. In this context, GeoAGI(Geospatial Artificial General Intelligence) has emerged as a new spatial intelligence paradigm that integrates generative AI into GIS and spatial analysis to emulate human-like geographic reasoning. In this study, we conducted a literature review to systematically explore the technological and conceptual transition from GeoAI to GeoAGI. We comprehensively analyze domestic GeoAI-related studies with recent work on geospatial foundation models, LLMs, and multimodal spatial intelligence. Based on our reviews, we defined the concept of GeoAGI and introduce its core components, including geospatial foundation model (GeoFM), geospatial knowledge graph(GeoKG), multimodal fusion architectures, spatial reasoning engines, and autonomous GIS agents. While GeoAGI shows substantial potential, it also faces critical challenges, including limited spatial reasoning accuracy, cross-modal inconsistency, hallucination, and regional bias. By identifying these issues and outlining key research directions, this study provides a theoretical foundation for advancing GeoAGI as a geospatial decision-support framework.

Keywords
GeoAGI
GeoAI
Generative Artificial Intelligence
Spatial Intelligence
Multimodal Geospatial Analysis

최근 대규모 언어모델(LLM)을 중심으로 한 생성형 인공지능의 발전은 공간정보 분야에서 다양한 방식으로 진화되고 있다. 기존의 GeoAI가 딥러닝 기반 영상 분석과 시공간 예측 등의 특정 과업에 최적화된 분석도구로 발전해 왔다면, 최근에는 자연어의 이해, 멀티모달 데이터 통합, 분석 절차의 자동화를 결합한 범용적 공간지능으로의 확장이 요구되고 있다. 이러한 흐름 속에서 GeoAGI(Geospatial Artificial General Intelligence)는 생성형 AI를 GIS 및 공간분석에 통합하여 인간의 지리적 추론 능력을 모방하는 새로운 공간지능 패러다임으로 주목받고 있다. 본 연구는 GeoAI에서 GeoAGI로의 기술적･개념적 진화를 체계적으로 정리하기 위해 문헌 고찰을 수행하였다. 국내의 GeoAI 관련 연구와 최근 국제 학술지에 게재된 공간 기초모델, 대규모 언어모델, 멀티모달 기반 공간지능 연구를 종합적으로 분석하여, 기술 발전 단계를 분석하였다. 이를 통해 GeoAGI의 개념을 정의하고, 공간기반 파운데이션모델(GeoFM), 공간지식그래프(GeoKG), 멀티모달 융합 구조, 공간추론 엔진, 자율형 GIS 에이전트 등의 핵심 구성요소를 소개하였다. GeoAGI는 많은 잠재력을 가진 반면에, 공간 추론의 정확도, 멀티모달 정합성, 환각, 지역성 편향 등의 기술적 한계도 존재한다. 본 연구는 이러한 한계를 극복하기 위한 핵심 연구 과제와 추진 전략을 제시하면서, GeoAGI의 학술적 정립과 공간정보 기반 의사결정 지원체계로의 확장을 위한 이론적 토대를 제공하고 있다.

키워드
GeoAGI
GeoAI
생성형 인공지능
공간지능
멀티모달 공간분석
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Information
  • Publisher :The Korean Geographical Society
  • Publisher(Ko) :대한지리학회
  • Journal Title :Journal of the Korean Geographical Society
  • Journal Title(Ko) :대한지리학회지
  • Volume : 61
  • No :1
  • Pages :55-68
  • Received Date : 2026-01-05
  • Revised Date : 2026-02-05
  • Accepted Date : 2026-02-09
  • DOI :https://doi.org/10.22776/kgs.2026.61.1.55
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