Evaluating intent-driven color generation: a framework for assessing LLM-assisted cartographic design

This study evaluates how stated analytical goals influence LLM-generated color schemes for thematic maps, assessing three perceptual dimensions: whether colors form a clear sequence matching the data hierarchy (order), whether categories are visually distinct from one another (differentiation), and whether related values share similar or thematically cohesive colors (association).

Using a synthetic dataset of 20 lakes classified by pollution level (Low, Medium, High), we tested three LLMs across 12 prompt conditions varying in task framing and analytical emphasis. GPT-5 demonstrated systematic intent sensitivity: order prompts produced cohesive color schemes, differentiation prompts generated multi-hue palettes with greater separation, and lightness encoding reversed depending on whether users emphasized high or low pollution.