Abstract:

In recent years, large language models (LLMs) have made remarkable progress in semantic understanding and task reasoning, providing new technological opportunities for the intelligent design of maps. This study focuses on the color design of administrative maps and proposes a framework, MapColor-Agent, that integrates large language models with a multi-agent collaboration mechanism. The framework employs the LLMs as the semantic reasoning core and enables task decomposition and process coordination through multiple agents. It also combines natural language interaction with a graphical user interface, allowing users to intuitively generate semantically consistent color schemes for maps. The system performance was evaluated using the system usability scale and semi-structured interviews. The results show that MapColor-Agent achieved an overall usability score of 77.9, indicating a good level of usability. Participants generally found the system easy to learn, clear in operation, and natural in interaction, with high levels of interpretability and controllability of results. Difference analysis revealed that participants familiar with map color design scored higher in learning efficiency and perceived complexity, suggesting that background knowledge influences user experience. The interview results further indicated that the framework performs well in semantic understanding and task guidance, though improvements are needed in complex semantic parsing and generation stability. Overall, the findings demonstrate the feasibility of integrating large language models with multi-agent collaboration for map color design and provide a reference for future research on semantic reasoning and multimodal interaction in intelligent cartographic design.

Key words: large language models, multi-agent system, human-computer interaction, map color design, administrative map