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Journal of Spatial Information Science
Article . 2025 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Opportunities and challenges of integrating geographic information science and large language models

Authors: Nico Van de Weghe; Lars De Sloover; Anthony Cohn; Haosheng Huang; Simon Scheider; Renée Sieber; Sabine Timpf; +1 Authors

Opportunities and challenges of integrating geographic information science and large language models

Abstract

The integration of large language models (LLMs) with geographic information science (GIScience) represents a new frontier in interdisciplinary research that combines advanced natural language processing with sophisticated spatial data analysis. This paper explores the synergistic potential of combining the natural language understanding and generation capabilities of LLMs with the expertise of GIScience in handling complex geospatial data. By exploring the specific contributions that LLMs can offer to GIScience, such as improving data processing, analysis, and visualization, and the mutual benefits that GIScience can offer to LLMs in terms of spatial reasoning and conceptual frameworks, we outline a comprehensive framework and a research agenda for this integration. Furthermore, we address the societal and ethical implications of this convergence, highlighting the challenges of bias, misinformation, and environmental impact. Through this exploration, we aim to set the stage for innovative applications in urban planning, environmental analysis, and beyond, while emphasizing the need for responsible use of AI.

Countries
Belgium, Netherlands
Keywords

Earth and Environmental Sciences, multimodal data integration, spatial reasoning, geographic information science (GIScience), large language models (LLMs)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
gold
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