从绘画到地图的笔触风格迁移方法

孙彦杰; 赖志诚; 周传龙; 吴明光

doi:10.13203/j.whugis20250209

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从绘画到地图的笔触风格迁移方法

地图设计的基本问题 | 更新时间：2026-11-05

- 从绘画到地图的笔触风格迁移方法
- A Brushstroke Style Transfer Method from Painting to Map
- 武汉大学学报（信息科学版） 2026年51卷第3期页码：463-474
- 作者机构：
  
  1.南京师范大学地理科学学院，江苏南京，210023
  2.中国电子科技集团公司第二十八研究所，江苏南京，210023
  3.南京师范大学虚拟地理环境教育部重点实验室，江苏南京，210023
  4.江苏省地理信息资源开发与利用协同创新中心，江苏南京，210023
- 作者简介：
  
  孙彦杰，博士，工程师，研究方向为地图风格迁移、地图设计等。yanjiesun@njnu.edu.cn
  吴明光，博士，教授。wmg@njnu.edu.cn
- 基金信息：
  
  国家自然科学基金面上项目(42371443)
- DOI：10.13203/j.whugis20250209
  中图分类号： P208
- 收稿：2025-07-15，
  
  纸质出版：2026-03-05
- 稿件说明：
移动端阅览
孙彦杰,赖志诚,周传龙，等.从绘画到地图的笔触风格迁移方法[J].武汉大学学报（信息科学版）,2026,51(3):463-474.

SUN Yanjie,LAI Zhicheng,ZHOU Chuanlong,et al.A Brushstroke Style Transfer Method from Painting to Map[J].Geomatics and Information Science of Wuhan University,2026,51(3):463-474.
孙彦杰,赖志诚,周传龙，等.从绘画到地图的笔触风格迁移方法[J].武汉大学学报（信息科学版）,2026,51(3):463-474. DOI：10.13203/j.whugis20250209

SUN Yanjie,LAI Zhicheng,ZHOU Chuanlong,et al.A Brushstroke Style Transfer Method from Painting to Map[J].Geomatics and Information Science of Wuhan University,2026,51(3):463-474. DOI：10.13203/j.whugis20250209

摘要

地图风格迁移是指将颜

色、纹理、笔触等视觉元素从绘画等作品转移到地图上的过程，好的风格迁移能够提升个性、创意地图制作的效率和质量。在绘画中，笔触包含了线条的粗细、纹理及笔画的方向等丰富的线状图形信息，是绘画风格的关键特征，也是艺术家表达情感的重要视觉元素。从绘画到地图的笔触风格迁移，有利于提升地图的视觉表现力，加强地图的形式美与情感表达效果，在城市印象地图、旅游地图等类型的泛地图制作方面有着迫切的技术需求。当前的地图风格迁移主要集中在颜色、纹理等方面，缺少针对笔触的地图风格迁移研究，通用的绘画笔触风格迁移方法通常忽略了地图空间关系保持与笔触风格表达之间的平衡，容易导致损失地图的信息负载及传输功能。为解决这一问题，基于扩散模型提出一种模拟绘画过程的从绘画到地图的笔触风格迁移方法。该方法借鉴人类绘画从结构框架到笔触填充的过程，并结合地图符号的绘制策略，区分符号几何维度和图形-背景关系。通过将笔触风格迁移视为扩散模型下的渐进式笔触匹配过程，能够在保留地图空间关系准确性的基础上实现笔触风格的自适应迁移。实验在多个指标上进行了评估，包括结构相似性、内容保真度、风格相似性与符号清晰性，结果显示，所提方法在多个方面均表现出较优的迁移效果（

0.05），有望丰富地图风格迁移的方法体系，为个性创意制图提供新的技术途径。

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 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 relationship 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 us

ed 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 (

0.05). In terms of symbol clarity

the proposed method outperformed the comparison 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 improvements (

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 relationship and symbol semantics. This approach also provides a feasible solution 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.

关键词

Keywords

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