论文用5,760张地图测了21个模型，发现配色顺序和对比度比色调更影响AI读图。做地图可视化或调模型时值得看一眼。
研究发现，色调选择对基础模型的空间推理影响有限且不稳定，而打乱序列配色顺序会显著降低模型性能，尤其在比较和排序任务中。研究构建了包含5,760张地图和28,800个问题的基准，评估21个开源及专有多模态模型。降低明度对比度同样持续损害推理能力，而过度增加对比度仅带来边际收益。LoRA微调提升了整体准确率，但保留了这些相对敏感性。实验还表明，错误源于颜色与图例解码、空间推理以及主题属性与空间结构的整合。
Toward AI-Friendly Cartography: Understanding How Color Design Influences Foundation Model Spatial Reasoning on Sequential Choropleth Maps
Foundation models (FMs) increasingly support multimodal and geospatial reasoning, yet it remains unclear whether cartographic principles designed for human perception are equally effective for machines. Focusing on sequential choropleth maps, we examine how hue palette, color ordering, and lightness contrast influence FM spatial reasoning. We construct a controlled benchmark of 5,760 maps and 28,800 questions spanning Attribute Identify, Spatial Recognition, Compare, Rank, and Pattern Delineate, and evaluate 21 open-source and proprietary multimodal FMs. Results show that hue choice has limited and inconsistent effects, whereas disrupting sequential color ordering substantially reduces performance, especially for comparison and ranking. Reduced lightness contrast also consistently impairs reasoning, while increasing contrast beyond sufficient separability provides only marginal gains. LoRA fine-tuning improves overall accuracy but preserves these relative sensitivities. Additional factorial experiments further indicate that errors arise from color-and-legend decoding, spatial reasoning, and the integration of thematic attributes with spatial structure. These findings show that conventional sequential ordering and sufficient contrast remain important for machine map understanding and provide empirical guidance for AI-friendly cartographic design.