LLM4GIS: Large Language Models for GIS

Dear Colleagues,

The integration of large language models (LLMs) into geographic information systems (GISs) marks a transformative shift in GeoAI. As the volume and complexity of spatial data continue to grow, LLMs offer a new paradigm for automating spatial analysis, enabling natural language interactions, and enhancing analytical reasoning within GIS environments. By leveraging recent advances in prompt engineering, retrieval-augmented generation (RAG), autonomous agents, and multi-agent frameworks, LLMs significantly extend the usability, scalability, and adaptability of GIS platforms across technical and non-technical user groups.

Despite the promising potential of LLMs in GIS, their deep integration still faces multiple challenges. Spatial data are inherently heterogeneous, structurally complex, and semantically layered, limiting the generalization capabilities of LLMs in understanding geographic entities, coordinate systems, and spatial contexts. Moreover, GIS analysis often involves multi-step, hierarchical tasks, while user intentions—especially from non-expert users—tend to be vague, discontinuous, or imprecise in natural language. LLMs must not only accurately infer such complex spatial intentions but also assist users in clarifying their goals through task reformulation, semantic guidance, and parameter suggestions. From a technical perspective, challenges remain in designing spatially aware token representations, integrating multimodal geospatial inputs (e.g., imagery, remote sensing, and sensor data), modeling spatial relations and topology within contextual embeddings, and improving reasoning controllability and interpretability without sacrificing spatial precision. Importantly, different GIS applications involve distinct underlying scientific problems, including trajectory modeling and prediction in transportation, map language understanding and visual semantics in cartography, and syntax design, workflow reasoning, and metadata integration in geospatial code generation. These diverse tasks impose higher demands on the expressive, inferential, and knowledge integration capabilities of LLMs, while also opening up rich avenues for research.

Given the above challenges, this Special Issue focuses on the innovative research of LLMs in GISs, welcoming contributions that span foundational methodologies to real-world implementations. Topics of interest include intelligent spatial data processing (such as automated spatial data cleaning and transformation, as well as intelligent geospatial report generation); multimodal data fusion (integrating imagery, sensor data, and textual information to enable the unified modeling of complex spatial semantics); advances in spatial analysis and interaction (including LLM-enhanced spatial analysis methods and natural language interfaces for simplifying GIS operations); applications of LLMs in geospatial decision support systems (covering domains such as urban planning, environmental monitoring, and climate modeling); model optimization strategies for geospatial tasks (such as domain-specific token design, fine-tuning methods, and alignment with spatial ontologies); and a range of emerging interdisciplinary directions, including geospatial code generation, map generation from textual input, trajectory forecasting, and knowledge-driven spatial dialogue systems.

We invite interdisciplinary contributions from artificial intelligence, spatial data science, data visualization, geospatial modeling, urban planning, and other relevant fields, aiming to drive the deep integration of LLMs and GIS. Topics to be considered include (but are not limited to) the following:

• LLMs for GIS data processing;
• LLMs for geospatial report generation;
• LLMs for multimodal GIS integration;
• LLMs for enhancing spatial data accessibility;
• LLMs in urban planning and environmental analysis;
• LLMs for natural language querying in real-time GISs;
• LLMs for improving GIS user experience;
• LLMs for GIS map generation;
• LLMs for geospatial question answering and intelligent querying;
• LLMs for geospatial code generation;
• LLMs for trajectory prediction and spatiotemporal analysis;
• LLMs for spatial database querying and NL interfaces;
• LLMs for causal inference in geospatial analysis;
• LLMs for explainable spatial reasoning;
• LLMs for user intent analysis and geospatial task decomposition.

We invite researchers in these fields to submit original research, methodological papers, case studies, or review articles. This Special Issue aims to highlight the significant applications of LLMs in GIS analysis and provide a platform for academic exchange and best practices in this innovative area.

Prof. Dr. Huayi Wu
Prof. Dr. Zhipeng Gui
Guest Editors

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