Abstract: Objectives: Map style transfer involves the integration of visual elements such as color, texture, and brushstrokes from artistic works into cartographic design. Among these, brushstrokes convey essential stylistic features and emotional tones through attributes like line thickness, directionality, and texture. Incorporating brushstroke styles into maps enhances their expressive capacity and aesthetic appeal, especially in personalized and thematic cartography such as urban impression maps and cultural or tourist maps. Existing research has largely focused on color and texture transfer, with limited attention to brushstroke-specific migration in a cartographic context. Methods: To address the lack of brushstroke-focused transfer methods to maps, a diffusion model-based approach is proposed that simulates the human painting process. First, control points are identified for various map elements: for point features, the coordinates are directly used; for line features, all vertices forming the line are selected; for polygonal and background features, contour characteristics are extracted using a self-attention mechanism and transformed into contour control points. These control points from point, line, area, and background elements are then unified to form a complete map framework control point set. Next, Bézier curves are generated from these control points and used as initial strokes for iterative optimization of the map framework. During optimization, a differentiable rasterizer converts Bézier curves into differentiable raster images, enabling the generation process to be integrated into deep learning models for further refinement. The optimization model takes the Bézier control points and opacity as inputs, using alpha blending to simulate stroke intensity and overlap effects, and applies an extended score distillation sampling (SDS) to minimize differences between the rasterized image and the input map. Parameters are iteratively updated until convergence to produce an accurate brushstroke-based map framework. Building on this, inspired by the What⁃Where dual-channel mechanism of human vision, specific loss functions are designed for different stroke types to jointly constrain the diffusion model's optimization in terms of spatial structure and stylistic features. The Where channel ensures spatial consistency between the generated and input maps, while the What channel evaluates stylistic similarity between the generated map and the reference painting. Finally, the diffusion model progressively matches and stylizes the map brushstrokes, achieving adaptive brushstroke transfer while preserving figure⁃ground relationships and symbol geometric precision. Results: The proposed method was evaluated against representative brushstroke transfer techniques through both quantitative and qualitative analyses across four key metrics: structural similarity, content fidelity, style consistency, and symbol clarity. For structural similarity, the structural similarity index measure (SSIM) was employed to quantitatively assess the generated maps, where the proposed method achieved an average SSIM score of 0.876 across two test maps, substantially higher than the comparison method's 0.333, indicating superior preservation of the spatial structure of map content. Regarding content fidelity, the content fidelity (CF) metric was used to evaluate the consistency of cartographic information before and after style transfer, with the proposed method achieving an average CF score of 0.911 compared to 0.551 for the comparison method, demonstrating higher precision in content restoration and information retention. To further examine perceptual differences in style transfer performance, a user study was conducted employing a 7-point Likert scale to evaluate style consistency and symbol clarity. In terms of style consistency, maps generated by the proposed method consistently received higher ratings than those of the comparison method, with mean improvements exceeding 1 point and all differences reaching statistical significance (p < 0.05). In terms of symbol clarity, the proposed method outperformed the com parison method across all test cases, achieving average improvements of over 3 points, with all results statistically significant. Overall, experimental results indicate that the proposed method outperforms the representative brushstroke transfer method across all four evaluation metrics, with statistically significant im provements (p < 0.05), particularly in maintaining the legibility and semantic accuracy of map elements under stylization. Conclusions: The proposed method employs diffusion-based modeling to guide the progressive transfer of brushstroke styles from paintings to maps, balancing the preservation of spatial structure and symbol semantics. This approach also provides a feasible approach for integrating brushstroke styles into maps, facilitating further exploration of the coordination of style and structure in cartographic design. Nevertheless, the method still has room for improvement regarding map information load, transmission efficiency, and brushstroke adaptability. Future research should further examine the impact of artistic style on map readability and information communication, and investigate the adaptability and transferability of reference artworks, to achieve an optimal balance between artistic expression and cartographic functionality.