Traditional map-making relies heavily on Geographic Information Systems (GIS), requiring domain expertise and being time-consuming, especially for repetitive tasks. Recent advances in generative AI (GenAI), particularly image diffusion models, offer new opportunities for automating and democratizing the map-making process. However, these models struggle with accurate map creation due to limited control over spatial composition and semantic layout. To address this, we integrate vector data to guide map generation in different styles, specified by the textual prompts. Our model is the first to generate accurate maps in controlled styles, and we have integrated it into a web application to improve its usability and accessibility. We conducted a user study with professional cartographers to assess the fidelity of generated maps, the usability of the web application, and the implications of ever-emerging GenAI in map-making. The findings have suggested the potential of our developed application and, more generally, the GenAI models in helping both non-expert users and professionals in creating maps more efficiently. We have also outlined further technical improvements and emphasized the new role of cartographers to advance the paradigm of AI-assisted map-making. The code and pre-trained models are available at https://github.com/claudaff/generative-ai-mapmaking/.

翻译：传统地图制图高度依赖地理信息系统（GIS），需要领域专业知识且耗时较长，尤其是在处理重复性任务时。生成式人工智能（GenAI）的最新进展，特别是图像扩散模型，为地图制图过程的自动化和普及化提供了新的机遇。然而，由于对空间构成和语义布局的控制有限，这些模型在创建精确地图方面面临挑战。为解决此问题，我们整合矢量数据，通过文本提示引导生成不同风格的地图。我们的模型首次实现了在受控风格下生成精确地图，并将其集成到网络应用程序中，以提高其可用性和可访问性。我们与专业制图师进行了用户研究，评估生成地图的保真度、网络应用程序的可用性，以及不断涌现的GenAI对地图制图的影响。研究结果表明，我们开发的应用程序以及更广泛的GenAI模型在帮助非专业用户和专业人员更高效地创建地图方面具有潜力。我们还概述了进一步的技术改进，并强调了制图师在推进AI辅助地图制图范式中的新角色。代码和预训练模型可在 https://github.com/claudaff/generative-ai-mapmaking/ 获取。