MapMate: A framework bridging natural language interaction and map design through large language models

• MapMate enables natural language-driven map design through LLM-based framework. • Hierarchical knowledge base integrates map design principles. • Memorable strategy maintains design context across multi-round interactions. • Human-AI collaboration reduces technical barriers and preserve design integrity. Map design is fundamental to geographic information communication and applications, yet it remains an expertise-intensive task involving complex operational procedures that limit accessibility for diverse users despite the proliferation of digital platforms. This study introduces MapMate, a large language model (LLM)-based framework that enables map design through natural language interaction. MapMate addresses the gap between modification goals expressed in natural language and technical map design configurations by integrating a hierarchical map design knowledge base with platform-specific specifications. The framework comprises four core components: a request validator that ensures cartographic parameter validity and operational feasibility, a map design task planner that decomposes goal-oriented requirements into executable operations, a context information retriever that maintains project coherence, and a map design tool router that orchestrates map design functions. To overcome LLM memory limitations in multi-round interactions, MapMate implements a persistence strategy that maintains records of operational history and map-wide design states. Three case studies validate the framework’s effectiveness across single-layer refinement, cross-layer design refinement, and context-aware map design. Results demonstrate that MapMate successfully bridges natural language interaction and map design, providing a human-AI collaborative environment that reduces technical barriers while maintaining cartographic integrity. This framework represents a promising advancement toward the development of intelligent, accessible map design systems for integration with AI-enhanced GIS applications.