Toward AI ‐Friendly Sequential Choropleth Maps: Understanding How Color Design Influences Foundation Model Spatial Reasoning

Recent advances in foundation models (FMs) have significantly improved multimodal reasoning and geospatial understanding, leading to growing interest in map‐based spatial cognition for FMs. However, most existing cartographic design principles were originally developed for human visual perception, and it remains unclear whether these principles are equally effective for FM reasoning. To address this gap, we focus on choropleth maps, one of the most widely used forms of thematic cartography, and systematically investigate how three classical cartographic factors—sequential hue palettes, sequential versus randomized color ordering, and lightness contrast—influence FM spatial reasoning. We construct a large‐scale benchmark containing 5760 choropleth maps with controlled spatial structures, together with 28,800 multi‐level spatial reasoning tasks spanning Attribute Identify , Spatial Recognition , Compare , Rank , and Pattern Delineate . Twenty‐one recent multi‐modal FMs from both open‐source and proprietary ecosystems are systematically evaluated. Experimental results reveal: First, different sequential hue palettes produce only a limited and non‐systematic influence on model performance, suggesting that FMs rely less on hue semantics than human map readers. Second, contrary to conventional assumptions, disrupting sequential color ordering substantially degrades spatial reasoning performance across most evaluated models, particularly for comparison‐ and ranking‐based tasks. Third, lightness contrast constitutes a fundamental machine‐readable signal: reducing contrast consistently harms reasoning performance, whereas further increasing contrast provides only marginal additional improvement once sufficient separability is achieved. To further assess whether these sensitivities are intrinsic or can be learned, we additionally perform lightweight LoRA fine‐tuning, which substantially improves overall geospatial reasoning while confirming that the relative sensitivity to sequential ordering and lightness contrast remains stable. These findings indicate that FMs depend heavily on conventional sequential color ordering and sufficient contrast during map understanding. Overall, our results suggest that some classical cartographic principles remain highly beneficial for machine spatial reasoning, while others transfer less effectively to machine cognition, providing empirical guidance for AI‐friendly cartography.