Recent breakthroughs in natural language understanding, reasoning, and code generation have opened a new path for the intelligent transformation of geographic information systems (GIS). Existing large language models (LLM) -driven GIS methods. However, largely remain task-specific and static, lacking the ability to accumulate knowledge and evolve autonomously. This paper proposes a self-evolving spatial intelligence framework that deeply integrates LLM with GIS for automated geospatial knowledge discovery and decision support. The framework begins with the construction of a geospatial knowledge graph, extracting, organizing, and continuously updating spatial knowledge from multi-source heterogeneous data through a reflexion-enhanced self-learning loop. Building on this, an autonomous spatial analysis agent converts natural-language intents into executable geospatial workflows through tool calling and multi-agent collaboration. Finally, a knowledge-augmented decision-support layer produces interpretable, evidence-based recommendations for spatial planning and decision-making. Together, these three components form a closed loop of knowledge discovery, decision support, and self-evolution, moving GIS from a passive analysis tool toward a continuously self-improving spatial intelligence system. Case studies show that the framework is effective in automating geospatial analysis, improving the accuracy and completeness of knowledge extraction, and enhancing decision-making efficiency, while the hallucination and reliability of LLM remain open challenges.
References
[1] Akinboyewa, T., Li, Z., Ning, H., Lessani, M. N. (2025) GIS copilot: towards an autonomous GIS agent for spatial analysis. International Journal of Digital Earth, 18(1), 2497489.
[2] Zhang, Y., Wei, C., He, Z., Yu, W. (2024) GeoGPT: an assistant for understanding and processing geospatial tasks. International Journal of Applied Earth Observation and Geoinformation, 131, 103976.
[3] Ning, H., Li, Z., Akinboyewa, T., Lessani, M. N. (2025) An autonomous GIS agent framework for geospatial data retrieval. International Journal of Digital Earth, 18(1), 2458688.
[4] Pisetta, J., Souza, F. A., Amorim, J., Antonio, N. D., Nunes, D. M., Araki, H., Camboim, S. P. (2026) Mapping with words: Integrating large language models into geospatial practice. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10, 285-292.
[5] Wang, C., Kang, Y., Gong, Z., Zhao, P., Feng, Y., Zhang, W., Li, G. (2025) CartoAgent: a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluation. International Journal of Geographical Information Science, 39(9), 1904-1937.
[6] Zhang, Y., Zhang, W., Zeng, Z., Jiang, K., Li, J., Min, W., Luo, W., Guan, Q., Lin, J., Yu, W. (2025) MapReader: a framework for learning a visual language model for map analysis. International Journal of Geographical Information Science, 39(9), 1973-2008.
[7] Yang, A., Cheng, F., Jia, Q., Dong, W., Ma, M., Chen, H., Yang, F., Wu, H. (2025) Evaluating and enhancing spatial cognition abilities of large language models. International Journal of Geographical Information Science, 39(9), 2009-2044.
[8] Feng, X., Cao, Y. (2025) Enhancing the accessi- bility of regionalization techniques through large language models: a case study in conversational agent guidance. International Journal of Geographical Information Science, 39(9), 2045-2068.
[9] Zheng, W., Yang, M., Ren, Y., Wang, H., Zeng, C., Zhang, Y. (2026) CARAG: context-aware retrieval-augmented generation for railway operation and maintenance question answering over spatial knowledge graph. ISPRS International Journal of Geo-Information, 15(2), 78.
[10] Zhang, J., Qi, Y., Che, Y., Yang, R., Guo, X. K., Zhang, H. (2026) Resolving semantic conflicts in multi-source threat intelligence through temporal consistency modeling. Information Sciences, 123267.
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Cheng, H. (2026) Self-evolving Spatial Intelligence – Integrating Large Language Models with GIS for Automated Geospatial Knowledge Discovery and Decision Support. Scientific Research Bulletin, 3(4), 20-24. https://doi.org/10.71052/srb2024/XKDR2247