The growing adoption of digital twins in geomatics sectors requires efficient 3D mapping techniques. However, the complexity and cost of producing cartographically enriched 3D scenes pose significant challenges, hindering widespread application, particularly in domains with limited mapping expertise and budgets. The proposed methodology in this paper leverages recent advances in natural language processing and artificial intelligence, particularly large language models (LLMs), to reduce the expertise required for 3D mapping and to address the high costs and complexity associated with traditional cartographic processes. It introduces a natural language interface for 3D symbology, aimed at simplifying the design and automating the creation of cartographically enriched 3D scene. By allowing cartographers converse with the mapping system, the system translates verbal descriptions into structured symbology rules in 3D digital cartographic model, which are then used to generate cartographically enriched 3D scenes. The method chains multiple ad-hoc LLM-based agents for entity linking, conversation handling, and symbology rule verification. Prompt engineering methods, such as chain-of-though and retrieval augmented generation, have been used to guide the agents’ reasoning process or leverage knowledge base, respectively. Experiment and application in utility networks demonstrates the method’s capability to accurately interpret and execute 3D symbology rules from natural language inputs, resulting in cartographically enriched 3D scenes that are reproducible and scalable. This work represents a pioneer study to implement a natural language interface for 3D mapping. It not only enhances the usability and accessibility of 3D mapping in digital twins but also sets a foundational method for future research in natural language-based mapping interfaces.