‘2. Literature review

2.1 Map style transfer
A map style refers to a collection of distinct yet aesthetically cohesive features in cartographic design (Kent & Vujakovic, 2017). Map style transfer refers to reproducing these styles, whether from an existing map, or an artistic work, onto new map data (Kang et al., 2024). Map style transfer is among the first several cartographic tasks on the artistic side of cartography that can be achieved using AI (Kang et al., 2019). The automatic map style transfer based on given inspiration sources has become a hot topic within the cartographic community and has attracted significant attention from researchers (Christophe et al., 2022; Wu et al., 2022a; Wu et al., 2022b). We categorize existing map style transfer approaches based on the type of map data processed: vector-based and raster-based map style transfer.
Vector data represents geographic entities as points, polylines, and polygons with precise location coordinates and associated attributes. While making maps using vector data, the styles are kept separate from the geographic information. To guide map design decisions, cartographers have developed cartographic grammars that specify style specifications and visual variables such as sizes, colors, and shapes (Diamond, 2019; Griffin, 2021; Nestel, 2019; White, 2017). For instance, researcher has used color pickers to extract color schemes from Monet’s landscape paintings and applied these schemes to vector data (Friedmannová, 2009). Christophe & Hoarau (2012) highlighted the importance of considering color proportions and complementary principles when designing style specifications. Wu et al. (2022a) developed an optimization algorithm for color transfer from arbitrary images to a vector map. These studies primarily focus on the color aspects of map styles while overlooking other elements, such as sizes and shapes, which are equally important in map design. Furthermore, current AI methods have not been fully leveraged in vector-based map style transfer, leaving opportunities to explore the integration of AI techniques for map style design.
A raster map typically consists of an array of pixels arranged in a rectangular grid, with each pixel storing numerical values that represent geographic information (Williams, 2019). The use of AI for map style transfer roots from raster-based maps. For example, some researchers developed advanced generative adversarial networks to transfer map images within identical geographical contexts and artistic paintings to another map (Kang et al., 2019; Christophe et al., 2022). Further, researchers have also explored the potential of transferring emotional nuances in paintings to maps (Bogucka & Meng, 2019), made efforts to transfer map styles from remote sensing images (Chen et al., 2020) and relief shading maps (Jenny et al., 2020, 2022), and to generate historical maps and game maps (Li, 2019; Li et al., 2021). One notable limitation of these studies refers to the fact that the generated maps may contain blurred areas and nonsensical symbols and annotations. Since AI models often lack a deep understanding of spatial relationships and structures, this may disrupt the topological relationships between map elements (Wu et al., 2022b; Kang et al., 2024).
Therefore, the proposed CartoAgent aims to tackle these challenges. We will take vector data as input, which allows for the separation of geographic data and styles. A specialized agent is designed to learn map styles from external inspirations, and another agent focuses on modifying and enhancing map styles. This enables CartoAgent to automate the map design process, adjust style specifications, and ensure that geographic information is accurate and well-maintained.
2.2 Map style evaluation
Map evaluation aims to enhance the quality and efficacy of cartographic design through critical analysis (Kang et al., 2024). In particular, map style evaluation focuses on guiding the map style transfer process to produce styled maps that are both visually appealing and accurate in conveying spatial information (Wu et al., 2022b). Current metrics for map style evaluation can be categorized as either objective or subjective.
Objective metrics. Early explorations in this field were inspired by image quality assessment methods from computer vision. Researchers often derived metrics from these methods to evaluate the quality of styled maps. For example, some researchers introduced the structural similarity index from computer science to assess the effectiveness of remote sensing imagery-to-map style transfer (Chen et al., 2020). More recently, researchers have begun integrating cartographic design constraints, such as smoothness and coalescence reduction, into the evaluation framework for styled maps (Courtial et al., 2022). Additionally, there have been efforts to directly develop geospatial artificial intelligence (GeoAI) models specifically for map style evaluation. For instance, Kang et al. (2019) curated natural images and maps to train a binary classifier, which determines whether distinct map characteristics are retained after map style transfer. These objective metrics are useful for conducting large-scale styled map evaluations. However, it is crucial to recognize the constraints associated with these metrics. On one hand, due to the inherent complexity of map design, these rule-based metrics often provide a shallow evaluation—focusing primarily on surface-level attributes, such as visual similarity, without capturing the deeper, more important aspects of cartographic quality, such as user perception or contextual relevance. On the other hand, scholars have pointed out that while machines can easily compute these metrics, users may struggle to interpret them and adjust their decisions accordingly (Kang et al., 2024).
Subjective metrics. Due to the limitations of objective metrics, researchers often conducted user studies. For instance, Chen et al. (2020) randomly selected graduates in GIS/Cartography to participate in a cognitive experiment by building an online platform where participants could evaluate styled maps. The statistical metrics derived from these evaluations were used to measure the effectiveness of the corresponding approaches. Additionally, by collecting a large volume of assessment data from participants, researchers have been able to train models that simulate user decision-making. This has led to the development of human-in-the-loop GeoAI models for map style evaluation (Kang et al., 2024). For example, in evaluating styled color quality, Chen et al. (2021) curated a dataset consisting of 781 five-color schemes, accompanied by assessments from 26 undergraduate students. An AdaBoost model was subsequently trained to align with the ratings provided by the undergraduates. Furthermore, Wang et al. (2024) utilized siamese neural networks to capture human judgments, resulting in predictive capabilities for assessing map quality. While these metrics offer a more in-depth assessment, they are not without challenges. On the one hand, such studies require considerable time and human resources. On the other hand, the aggregation of diverse perspectives and preferences from different experts in these methods may lead to an averaging effect, potentially overlooking personal individual differences in design preferences.
2.3 MLLMs and their applications in GIS/Cartography
LLMs have demonstrated impressive capabilities across various tasks (Chowdhery et al., 2023; Touvron et al., 2023). Researchers are actively exploring ways to leverage these models in the fields of GIS and Cartography (Mai et al., 2022). For instance, some have attempted to utilize LLMs to design thematic maps using publicly available geospatial data or to create mental maps from textual descriptions (Tao & Xu, 2023). Additionally, others are investigating methods to effectively instruct LLMs to generate spatial database queries (Jiang & Yang, 2024).
Recently, MLLMs that could enhance the capabilities of LLMs by incorporating multi-sensory skills, have attracted significant attention. Given the predominant role of vision in human perception, much of the research on MLLMs begins with the augmentation of visual capabilities. These approaches either fine-tune visual encoders to align them with parameter-frozen LLMs or utilize visual language models to translate visual inputs into textual descriptions comprehensible to LLMs (Zhu et al., 2023; Zeng et al., 2022). Some scholars have already begun to test both the general and domain-specific vision-language capabilities of MLLMs. For general vision-language capability evaluations, researchers typically employ various methods, including multiple-choice questions and interactive chatbot arenas, to assess the perceptual, comprehension, applicative, analytical, and creative abilities of MLLMs (Yin et al., 2023; Xu et al., 2023). Furthermore, researchers have investigated the potential of MLLMs in the field of geography. For example, researchers have reviewed the current landscape of vision-language models in remote sensing, envisioning the tremendous value that MLLMs could bring to visual tasks in this domain (Li et al., 2024). Additionally, the exploration of GPT-4V’s capabilities extends to cartography, where researchers have tested its ability to read and analyze maps. Preliminary findings suggest that GPT-4V can effectively extract information from maps (Xu & Tao, 2024). However, these initial experiments were relatively basic, and more in-depth investigations into how MLLMs can be integrated into map evaluation processes would be highly valuable.
2.4 Autonomous agents and their applications in GIS/Cartography
An autonomous agent is a system that interacts with its environment, perceiving its surroundings and taking actions over time to achieve its own goals (Wang et al., 2023). The development of powerful autonomous agents has long been a goal for both industry and academia. However, earlier efforts often focused on deploying models in isolated environments with limited knowledge, which differed significantly from human learning processes and made it difficult for agents to emulate human-like decision-making. Recently, the acquisition of extensive web knowledge has enabled MLLMs to demonstrate impressive capabilities. This progress has caught the attention of the autonomous agent community, leading to extensive explorations by researchers. Over time, a relatively unified framework has been developed and applied across various domains. 
Researchers have developed a wide range of autonomous agents, specializing in tasks for different domains. In the social sciences, autonomous agents have driven advancements in law, politics, and economics (Cui et al., 2023; Horton, 2023). For example, ChatLaw, a legal agent based on LLMs, provides effective legal consultation through database support and keyword searches (Cui et al., 2023). In the natural sciences, researchers are exploring the integration of autonomous agents with chemistry, resulting in the ChemCrow model, which assists researchers in chemical experiments (Bran et al., 2023). In civil engineering, an interactive framework has been developed where human architects collaborate with agents to build 3D structures (Mehta et al., 2023). Additionally, the LangChain framework has been used to develop agents that autonomously handle geographical data collection, processing, and analysis (Li & Ning, 2023; Zhang et al., 2024). These successful examples suggest that integrating autonomous agents into automated map design may enhance the user-friendliness of the system.
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