CartoAgent: a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluation.
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| Title: | CartoAgent: a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluation. |
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| Authors: | Wang, Chenglong1,2 (AUTHOR), Kang, Yuhao3,4 (AUTHOR), Gong, Zhaoya1,2 (AUTHOR) z.gong@pku.edu.cn, Zhao, Pengjun1,2 (AUTHOR), Feng, Yu5 (AUTHOR), Zhang, Wenjia6 (AUTHOR), Li, Ge7 (AUTHOR) |
| Source: | International Journal of Geographical Information Science. Sep2025, Vol. 39 Issue 9, p1904-1937. 34p. |
| Subjects: | Cartography, Map design, Language models, Generative artificial intelligence, Multimodal user interfaces, Aesthetics of art, Multiagent systems |
| Abstract: | The rapid development of generative artificial intelligence (GenAI) presents new opportunities to advance the cartographic process. Previous studies have either overlooked the artistic aspects of maps or faced challenges in creating both accurate and informative maps. In this study, we propose CartoAgent, a novel multi-agent cartographic framework powered by multimodal large language models (MLLMs). This framework simulates three key stages in cartographic practice: preparation, map design, and evaluation. At each stage, different MLLMs act as agents with distinct roles to collaborate, discuss, and utilize tools for specific purposes. In particular, CartoAgent leverages MLLMs' visual aesthetic capability and world knowledge to generate maps that are both visually appealing and informative. By separating style from geographic data, it can focus on designing stylesheets without modifying the vector-based data, thereby ensuring geographic accuracy. As a result, the proposed CartoAgent could effectively produce maps that are not only visually appealing but also accurate and informative. We applied it to a specific task centered on map restyling, namely, map style transfer and evaluation. The effectiveness of this framework was validated through extensive experiments and a human evaluation study. CartoAgent can be extended to support a variety of cartographic design decisions and inform future integrations of GenAI in cartography. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | The rapid development of generative artificial intelligence (GenAI) presents new opportunities to advance the cartographic process. Previous studies have either overlooked the artistic aspects of maps or faced challenges in creating both accurate and informative maps. In this study, we propose CartoAgent, a novel multi-agent cartographic framework powered by multimodal large language models (MLLMs). This framework simulates three key stages in cartographic practice: preparation, map design, and evaluation. At each stage, different MLLMs act as agents with distinct roles to collaborate, discuss, and utilize tools for specific purposes. In particular, CartoAgent leverages MLLMs' visual aesthetic capability and world knowledge to generate maps that are both visually appealing and informative. By separating style from geographic data, it can focus on designing stylesheets without modifying the vector-based data, thereby ensuring geographic accuracy. As a result, the proposed CartoAgent could effectively produce maps that are not only visually appealing but also accurate and informative. We applied it to a specific task centered on map restyling, namely, map style transfer and evaluation. The effectiveness of this framework was validated through extensive experiments and a human evaluation study. CartoAgent can be extended to support a variety of cartographic design decisions and inform future integrations of GenAI in cartography. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 13658816 |
| DOI: | 10.1080/13658816.2025.2507844 |
