CartoAgent: a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluation

TL;DR

CartoAgent is a multi-agent framework utilizing multimodal large language models to enhance map style transfer and evaluation, balancing artistic style and geographic accuracy through collaborative AI agents.

Contribution

This study introduces CartoAgent, a novel multi-agent system powered by multimodal large language models for improved cartographic design and evaluation.

Findings

Effective map style transfer demonstrated through experiments

Human evaluation confirms high-quality map aesthetics and accuracy

Framework supports diverse cartographic design decisions

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. We applied CartoAgent 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.