arXiv:2508.18959v2 Announce Type: replace 
Abstract: 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/.

يتناول المقال دمج الذكاء الاصطناعي التوليدي في رسم الخرائط، مشيرًا إلى إمكانيته في أتمتة وتيسير عملية تعتمد تقليديًا على نظم المعلومات الجغرافية. على الرغم من التقدم، تواجه نماذج الذكاء الاصطناعي التوليدي تحديات في إنشاء خرائط دقيقة بسبب القيود في التركيب المكاني والتخطيط الدلالي. يقدم المؤلفون نموذجًا يولد خرائط دقيقة بأساليب محددة، تم التحقق منها من خلال دراسات مستخدمين مع خبراء الخرائط، مما يبرز آثار الذكاء الاصطناعي التوليدي في هذا المجال.

El artículo discute la integración de la IA generativa en la elaboración de mapas, destacando su potencial para automatizar y democratizar un proceso que tradicionalmente depende de los Sistemas de Información Geográfica (SIG). A pesar de los avances, los modelos de IA generativa enfrentan desafíos para crear mapas precisos debido a limitaciones en la composición espacial y el diseño semántico. Los autores presentan un modelo que genera mapas precisos en estilos controlados, validado a través de estudios de usuarios con cartógrafos profesionales, enfatizando las implicaciones de la IA generati…

L'article aborde l'intégration de l'IA générative dans la cartographie, soulignant son potentiel à automatiser et démocratiser un processus traditionnellement dépendant des systèmes d'information géographique (SIG). Malgré les avancées, les modèles d'IA générative rencontrent des défis pour créer des cartes précises en raison de limitations dans la composition spatiale et la mise en page sémantique. Les auteurs présentent un modèle qui génère des cartes précises dans des styles contrôlés, validé par des études utilisateurs avec des cartographes professionnels, soulignant les implications de l'…

The article discusses the integration of generative AI in map-making, highlighting its potential to automate and democratize the process traditionally reliant on Geographic Information Systems (GIS). Despite advancements, generative AI models face challenges in creating accurate maps due to limitations in spatial composition and semantic layout. The authors present a model that generates precise maps in controlled styles, validated through user studies with professional cartographers, emphasizing the implications of generative AI in the field.

Generative AI in Map-Making: A Technical Exploration and Its Implications for Cartographers

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