An Assessment of the Map-Style Influence on Generalization with CycleGAN: Taking Line Features as an Example

As the complexity of GIS data continues to increase, there is a growing demand for automated map generalization. As end-to-end generative models, GAN models offer new solutions for automated map generalization. This study explores the impact of different map symbolization configurations on generative models, specifically using CycleGAN for line feature generalization. The quality of the generated results was assessed by constructing various symbolization datasets (line width, type, and color) and evaluating CycleGAN’s performance using metrics such as the MSE, SSIM, and PSNR. The results indicate that moderate line widths (0.5–1) yield better detail preservation, and different line types (framed lines and dashed lines) can highlight feature boundaries and enhance visual perception. By contrast, high-contrast color schemes enhance feature differentiation but increase pixel-level errors. This study concludes that generative models can maintain the geometric structure and spatial distribution of line features, but it is crucial to choose more suitable line features for different scenarios to meet detail requirements, ensuring high-quality outputs under diverse configurations.