Methodological study demonstrates robust natural-language-driven thematic map generation via semantic-template mapping, indicating reliable cartographic automation without rule violations.
Key Points
Develop a semantic-template mapping approach that bridges the gap between the probabilistic reasoning of large language models and the deterministic constraints required for professional thematic cartography.
Defined a four-dimensional cartographic template encompassing map type, application scenario, thematic semantics, and visual density.
Engineered a hybrid intention parsing mechanism to convert unstructured natural language instructions into concrete template configurations.
Developed a rule-embedded component-based renderer to generate visual thematic maps from the resulting specifications.
Achieved a maximum intention parsing accuracy of 90.8% on benchmark dataset evaluations.
Maintained a 100% cartographic validity rate across all generated thematic map outputs.