TL;DR
This paper explores the use of generative AI, specifically diffusion models guided by vector data, to automate and control map-making, demonstrating improved accuracy and usability for both experts and non-experts.
Contribution
It introduces a novel method integrating vector data with diffusion models for controlled map generation and provides a web application for practical use, assessed through a user study.
Findings
Generated maps in controlled styles show high fidelity.
The web application improves usability for cartographers.
GenAI models can assist non-experts in map creation.
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…
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