An Automatic Cartography Framework Integrating Knowledge Graph and Large Language Model

Objectives: Previous research has explored the applications of Generative AI in cartographic tasks, such as using image translation models for map style transformation, employing image diffusion models to convert textual descriptions into map images, and utilizing large language models (LLMs) as cartographic assistants to understand user requirements and operate professional cartographic tools. These studies have demonstrated the feasibility of applying Generative AI to cartographic tasks, although issues such as inaccuracies in map generation and the absence of cartographic knowledge persist. Methods: In response to the problem of the lack of cartographic knowledge in existing generative artificial intelligence for map-making tasks, a cartographic framework that integrates cartographic rules is proposed. The cartographic knowledge graph is taken as an external knowledge base, and based on the ReAct framework, the automatic reasoning of cartographic strategies, map generation, and user-interactive modifications are to be achieved. After analyzing and evaluating the cartographic capabilities of existing large language models, it is found that there are problems such as a scarcity of cartographic concepts, unstable content output, and a lack of computing and operational capabilities when directly using large language models for map-making tasks. Inspired by the existing cartographic process, the overall cartographic process of cartographic strategy generation, interactive modification, map result generation is designed, and a cartographic framework is determined, with the knowledge graph as the decision-making basis, the large language model as the decision-making agent, and Langchain and cartographic plugins as the decisionmaking executors. Results: The framework achieves an intelligent cartographic process encompassing strategy generation, interactive modification, and map result generation. Conclusions: Experimental results validate the effectiveness of incorporating cartographic knowledge in AI-generated map tasks. Future work will focus on enhancing map datasets and exploring spatial representation methods for geospatial data.