# GeoAI for map generalization in multi-scale cartography: foundations, a research agenda, and interdisciplinary perspectives

Zhiyong Zhou, Cheng Fu, Yu Feng, Guillaume Touya, Monika Sester, Robert

Weibel

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Zhiyong Zhou, Cheng Fu, Yu Feng, Guillaume Touya, Monika Sester, et al. GeoAI for map generalization in multi-scale cartography: foundations, a research agenda, and interdisciplinary perspectives. International Journal of Geographical Information Science, 2026, pp.1 - 29. 10.1080/13658816.2026.2626935. hal-05518674

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# GeoAI for map generalization in multi-scale cartography: foundations, a research agenda, and interdisciplinary perspectives

Zhiyong Zhou, Cheng Fu, Yu Feng, Guillaume Touya, Monika Sester & Robert Weibel

**To cite this article:** Zhiyong Zhou, Cheng Fu, Yu Feng, Guillaume Touya, Monika Sester & Robert Weibel (18 Feb 2026): GeoAI for map generalization in multi-scale cartography: foundations, a research agenda, and interdisciplinary perspectives, International Journal of Geographical Information Science, DOI: 10.1080/13658816.2026.2626935

**To link to this article:** https://doi.org/10.1080/13658816.2026.2626935

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Published online: 18 Feb 2026.

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FORESIGHT PAPER

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# GeoAI for map generalization in multi-scale cartography: foundations, a research agenda, and interdisciplinary perspectives

Zhiyong Zhou<sup>a,b</sup> ID, Cheng Fu<sup>a,c</sup> ID, Yu Feng<sup>d,e</sup> ID, Guillaume Touya<sup>f</sup> ID, Monika Sester<sup>g</sup> ID and Robert Weibel<sup>a,h</sup> ID

<sup>a</sup>Department of Geography, University of Zurich, Zurich, Switzerland; <sup>b</sup>Department of Geography, University of Wisconsin-Madison, Madison, USA; <sup>c</sup>College of Information and Electrical Engineering, China Agricultural University, Beijing, China; <sup>d</sup>IN3mainz - Institute for Spatial Information and Surveying Technology, University of Applied Sciences Mainz, Mainz, Germany; <sup>e</sup>Chair of Cartography and Visual Analytics, Technical University of Munich, Munich, Germany; <sup>f</sup>LASTIG, IGN, ENSG, University Gustave Eiffel, Champs-sur-Marne, France; <sup>g</sup>Institute of Cartography and Geoinformatics, Leibniz University Hannover, Hannover, Germany; <sup>h</sup>UZH Healthy Longevity Center, University of Zurich, Zurich, Switzerland

## ABSTRACT

Multi-scale spatial representation is a crucial mechanism for describing facts about the real world and for managing and communicating spatial data efficiently and effectively. To enable such multi-scale spatial representation, map generalization has thus been developed. The advances of GeoAI, which focus on spatially oriented deep learning and process understanding with AI techniques, have brought about a new paradigm for map generalization. In this foresight paper, we propose a map generalization framework supported by GeoAI techniques and review the evolution of GeoAI for map generalization. Taking into account the full process of producing maps or cartographic data products, we further highlight the research opportunities of GeoAI for map generalization and computational cartography. The potential technical challenges of GeoAI for map generalization are also noted for a longer-term exploration, including high-quality datasets, integrated generalization solutions, cartographic knowledge transfer, and large language models for map generalization. Finally, map generalization, as a fundamental process of spatial representation and reasoning, is expected to inspire, and be inspired by, other research strands regarding the use of novel GeoAI methods, including location encoding, digital twins, historical map interpretation, movement data management and mining, and temporal change detection of maps.

## ARTICLE HISTORY

Received 1 October 2025

Accepted 31 January 2026

## KEYWORDS

Map generalization; GeoAI; multi-scale cartography; deep learning; research agenda

## 1. Introduction

Map generalization is a fundamental cartographic activity that determines the information communicated on a map at certain scales (Mackaness 2007). It is an intellectual
<figure><img src="image_10.png" /></figure>

process that involves a series of analyses of geographic phenomena and the decision-making to abstract those phenomena at different spatial scales (Brassel and Weibel 1988, Mackaness 2007). While remote sensing with satellites and drones has enabled the mapping of the Earth with high spatial and temporal resolution, understanding the measured geographical phenomena still needs maps at different scales as a medium for communicating and perceiving spatial knowledge. Automating the process of map generalization is highly desired to free the labor-intensive efforts of expert cartographers in producing multi-scale maps and spatial data products, and enhance the general usage and generation of high-quality maps.

However, automated map generalization is challenging due to the complexity of geographic spaces and the difficulty of formalizing generalization knowledge (Weibel 1991). Since the 1960s, automated map generalization has experienced a series of paradigm shifts (Harrie and Weibel 2007), starting off with algorithmic solutions for specific operations, e.g., line simplification (Douglas and Peucker 1973, Li and Openshaw 1992). This was followed by a period since the 1990s when researchers began to develop integrated generalization solutions using several operators, relying on approaches that allowed to formalize the cartographic knowledge required for map generalization (Ruas and Plazanet 1996). These attempts towards modeling the overall process, including condition-action modeling, human interaction modeling, and constraint-based optimization approaches (Tobler 1964, Douglas and Peucker 1973, McMaster 1986, Weibel 1991, Ruas and Plazanet 1996, Weibel and Dutton 1998, Ruas and Duchène 2007), were partly supported by early use of machine learning (Weibel et al. 1995, Plazanet et al. 1998) and neural networks (Sester 2001). Although the degree of automation in the map generalization process increased with every paradigm shift (Stoter et al. 2014), the essential “knowledge acquisition bottleneck” persisted (Weibel et al. 1995). That is, the (in)ability to formalize—make explicit—the cartographic knowledge that determines the necessary generalization operators, processes, and evaluation criteria has remained a key limiting factor in the continued development of automated map generalization solutions.

The emergence of deep learning (DL) has offered a new opportunity to overcome this bottleneck (Touya et al. 2019, Sester et al. 2018, Feng et al. 2019, Harrie et al. 2024, Kang et al. 2024) as DL has shown a remarkable capacity in learning implicit knowledge inherent to large quantities of example data. However, it should be noted that these example data inevitably contain generalization biases as they are likely the product of different expert cartographers. This is because, in order to meet the same user requirements and finite map specifications at certain scales, expert cartographers may develop different map generalization solutions, including the selection of operators and the process of chaining operators (Rieger and Coulson 1993, Bard 2004, Stoter et al. 2014). In other words, there are usually multiple satisfactory map generalization solutions that may exist in multi-scale cartography, making it difficult to optimize and evaluate DL models for learning cartographic knowledge. Hence, there are significant challenges still remaining for DL in fully automating map generalization.

In recent years, artificial intelligence technologies, including DL, have increasingly been used to address geospatial problems, giving rise to geospatial artificial intelligence (GeoAI), which is becoming a cutting-edge technology to address massive
<figure><img src="image_11.png" /></figure>

geospatial problems, such as transportation predictions, humanitarian assistance, disaster response, etc. (Gao et al. 2023). Theoretically, automated map generalization, as a fundamental and classical problem of geographic information computing, should have great potential to advance GeoAI theories and methods (Mai et al. 2025), because map generalization addresses multiple representations of data, in this case data at different scales. Furthermore, according to Minsky (2000), looking at objects, phenomena, or processes in multiple ways is essential for their understanding. Thus, dealing with different representations in generalization can enrich the underlying knowledge that is learned through an AI process. However, the relevance and contributions of automated map generalization to GeoAI have not yet been systematically investigated, although some literature reviews have mentioned this topic in the broader context of AI in cartography (Harrie et al. 2024, Kang et al. 2024). Recently, Yan et al. (2025) conducted a comprehensive review of DL methodologies used for automated map generalization. While their article introduced and reviewed the fundamental applications of DL in map generalization, the opportunities and challenges of automated map generalization for further developing GeoAI methodologies have not been discussed in great depth. Furthermore, the interdisciplinary research opportunities originating from GeoAI-enabled map generalization have also not been fully demonstrated, suggesting that the broad practical implications of map generalization principles and methods to GeoAI are yet to be extensively recognized.

This foresight paper aims to take the previous review articles a step further and discuss the role of GeoAI in automated map generalization more specifically and, conversely, show how map generalization can inspire the further evolution of GeoAI. We thus hope to spark broader interest in this topic among researchers beyond the immediate field of cartography, as we are convinced that map generalization presents challenging problems with the potential to significantly enhance current GeoAI.

## 2. Evolving deep learning tasks for map generalization

To date, despite only a short history, deep learning has already been extensively applied to improve many processes in automated map generalization. However, a pure DL-based end-to-end solution for map generalization has not yet been established. Having reviewed the evolving literature on DL tasks for map generalization, we conceptualize GeoAI-enabled map generalization by the framework illustrated in Figure 1. In this foresight paper, we use this conceptual framework to review the literature on DL-driven map generalization, while more details about the technical basics of deep learning in automated map generalization can be found in Yan et al. (2025). Given a series of maps at multiple scales, the overarching goal of DL in map generalization is to overcome the cartographic knowledge acquisition bottleneck (Weibel et al. 1995, Mustière 2005) by learning more complete and coherent generalization knowledge that is hard to formalize by expert cartographers. As shown in Figure 1, an initial step following input maps at paired (i.e., source and target) scales is cartographic feature engineering. Geometric, structural, and semantic features are either designed manually or extracted through representation learning. Afterward, one DL-driven map generalization solution is end-to-end learning by inputting map features
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<figure><img src="image_13.png" /><figcaption>**Figure 1.** A DL-driven map generalization framework, with each task marked with its research status, as estimated by the authors.</figcaption></figure>

at a source scale and outputting generalized maps at a target scale. On the other hand, the entire DL-driven map generalization solution can also consist of a pipeline composed of multiple modules, while each module can be implemented by training an individual DL model. The following subsections will detail the related work on each DL task for map generalization.

## 2.1. Cartographic feature engineering

Cartographic feature engineering captures the essential characteristics of individual map objects and the relationships between different map objects on raw vector or raster maps. Expert cartographers have manually designed many measures to describe geometric, structural, and semantic features (Regnauld and McMaster 2007). Specifically, geometric features refer to geometric properties of individual map objects, such as the location of objects, the area of polygonal objects, and the sinuosity of linear objects, while structural features characterize the spatial structures composed of multiple map objects, such as the density of map objects given in a certain area. Furthermore, semantic features are those non-spatial attributes describing individual map objects, such as their name, object type, or functional importance.

By contrast, representation learning advances the characterization of spatial patterns of map objects with less or even without the manual effort of cartographers. Several polygonal geometric representation learning approaches have been proposed to capture shape features (Yan et al. 2021, Wang et al. 2023a, Mai et al. 2023, Knura
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2024, Yu et al. 2024), regularity of building groups (Yan et al. 2019, Liu et al. 2025), intersections of road networks (Li et al. 2024), and topological relationships (Siampou et al. 2024). A detailed review of spatial representation learning can be found in Mai et al. (2022). Furthermore, visual patterns represented on maps on certain scales, such as the regularity of building groups (Yan et al. 2019) and the categories of drainage networks (Yu et al. 2022), can be recognized through DL models to enrich domain knowledge for downstream formal generalization operations.

## 2.2. End-to-end learning

An end-to-end learning solution aims to produce generalized maps at the target scale by inputting engineered features of maps at the source scale in a single process, in which generalization operators are implicitly learned, and parameters controlling the operators are free of manual tuning from expert cartographers. Such a solution is applied mainly to raster maps (Touya et al. 2019). Early attempts have mainly applied original convolutional neural networks (CNNs) used for semantic segmentation, such as U-Net and generative adversarial networks (GAN), to predict generalized raster maps at the target scale based on input raster maps at the source scale (Feng et al. 2019, Courtial et al. 2020, Du et al. 2022a). However, these tasks do not fully fit the nature of map generalization, which extends further to the geometric transformation of map objects across scales. To address this limitation, cartographic constraints were introduced into loss functions in the optimization of neural networks (Kang et al. 2020, Courtial et al. 2023). Zhou et al. (2024) incorporated a boundary distance map into the discriminator of a GAN to enhance its ability to capture the fine-grained difference between predicted and ground-truth generalized maps.

## 2.3. Pipeline-based learning

A pipeline-based learning solution decomposes a complex task into a set of modules, each of which is learned by a certain neural network. In the context of map generalization, the pipeline typically consists of the selection of the generalization operator type, operator implementation, and overall process modeling to integrate multiple operators. Such a learning paradigm is particularly suitable for vector maps, where individual map objects are explicitly and compactly represented.

### 2.3.1. Operator type selection

This module decides which generalization operators should be applied to a certain map object. Conventional methods follow specific rules formulated by expert cartographers or interact with users through computer systems to select the appropriate operators for individual map objects (Weibel 1991, Harrie and Weibel 2007). The emergence of ML and DL significantly reduced manual efforts in this process by tackling the selection of operator type as a classification problem. For example, Lee et al. (2017) employed three ML classification algorithms to select the elimination or aggregation operator for individual buildings. Yan et al. (2022) utilized the building features extracted by a graph convolutional neural network (GCNN) to decide whether
<figure><img src="image_15.png" /></figure>

individual buildings should be grouped. Likewise, Niu et al. (2024) applied a GCNN to determine building generalization operators, including deletion, simplification, aggregation, and typification. However, little research focuses solely on this module, and most studies typically integrate the selection and implementation of operators as a compound task.

### 2.3.2. Operator implementation

This module aims to execute generalization operators for individual map objects. The operator implementation can be categorized into *DL-assisted implementation* and *DL-based implementation* according to the role of DL in implementation.

**DL-assisted implementation:** Given a set of candidate algorithms and parameters for a certain generalization operator, such as the Douglas-Peucker algorithm (Douglas and Peucker 1973) or the Visvalingam-Whyatt algorithm (Visvalingam and Wyatt 1993) for the polyline simplification operator, DL serves to select the most appropriate algorithm or parameters. Although in this approach, DL only has an assistive role, while the actual generalization operations are carried out by conventional algorithms, there are only a few relevant studies using this method. For example, Lagrange et al. (2000) introduced an early artificial neural network to determine the most appropriate parameter range for the smoothing operator. Yang et al. (2022) trained a backpropagation neural network to select an appropriate simplification algorithm for individual buildings from four candidate algorithms. As marked in Figure 1, the research on this task remains at the very early stage. We therefore encourage more implementations of DL-assisted operator and parameter selection for map generalization, which is guided by more cartographic knowledge and thus more controllable for generalizing maps at multiple scales.

**DL-based implementation:** This implementation directly enables DL models to output maps by applying certain operators. Existing implementations are focused on the selection and simplification operators. The selection operator is readily modeled as a classification task in which DL-based classifiers can be trained (Karsznia and Weibel 2018, Yu et al. 2023, Xiao et al. 2024b). The DL implementations for the simplification operator vary between polyline map objects (e.g., coastlines and DEM contour lines) and polygonal map objects (e.g., buildings). For polyline simplification, most implementations tackle the removal of redundant vertices or segments, thus resulting in DL classification models under supervised training (Du et al. 2022b, Xiao et al. 2024a, Jiang et al. 2023). Some other studies applied an unsupervised encoder-decoder architecture to derive simplified polylines (Yu and Chen 2022, Yan and Yang 2024). In contrast, the implementation of polygon simplification has received less attention, as it should not only remove redundant vertices but also preserve area properties (Tong et al. 2015) and maintain positional displacement of simplified buildings or other naturally occurring map objects (Kronenfeld et al. 2020), which is thus more challenging. To achieve this purpose, Zhou et al. (2023) formulated building simplification as the joint operation of two elementary vertex-level operators, vertex removal and vertex movement, and proposed a multi-task learning GCNN to classify the removal labels and regress movement vectors simultaneously. Moreover, Feng et al. (2023), Cui et al.
<figure><img src="image_16.png" /></figure>

(2025) introduced transformer architectures to enable end-to-end simplifications of vector buildings.

### 2.3.3. Process modeling

Process modeling, following the selection and implementation of individual generalization objects, refers to the compilation of a set of operators with specific parameters for each map object to obtain a legible map product (Regnauld et al. 2014). Conventional approaches have largely utilized rule-based modeling and constraint-based modeling (Harrie and Weibel 2007). The former approach follows expert cartographers' heuristics and applies certain operators under certain conditions, while the latter defines cartographic constraints and uses optimization techniques, such as multi-agent systems (Barrault et al. 2001, Ruas and Duchêne 2007) and combinatorial optimization (Neun et al. 2009), to implement the process modeling. However, so far there have not yet been relevant studies attempting to use DL to implement this module in a pipeline-based learning approach of map generalization.

## 3. Research agenda of GeoAI for map generalization

Despite the rapid advance of GeoAI and a plausible conceptual DL-driven map generalization framework as identified in the previous section, some critical gaps remain to be addressed to consolidate the identified framework. Accordingly, in this section, we outline a research agenda and expect to advance the development of GeoAI for map generalization from the perspectives of data curation, DL methodology, learnable generalization tasks, and evaluations.

### 3.1. High-quality dataset preparation for learning

One of the crucial characteristics of the AI paradigm is that it is data-intensive and requires diverse but balanced data input to train an effective DL model (Hey et al. 2009, Gahegan 2020). In principle, having maps at different scales implies that there are a lot of training data available. This is also true for certain applications; however, for certain architectures and models, a more fine-grained representation of the input and output data is necessary. Furthermore, in the context of map generalization, individual map objects are typically exposed to different types and combinations of generalization operators across scales, which may lead to a significantly imbalanced generalization dataset if this fact is not taken into account. For example, in the maps of swisstopo, the Swiss national mapping agency, by explicitly identifying map generalization operators during the scale transitions from 1:10,000 to 1:25,000, 1:50,000, and 1:100,000 consecutively (Senn et al. 2024), it is found that the prevalence of generalization operators varies across scales (Figure 2). Hence, an essential step when building training datasets for DL models in map generalization requires explicitly annotating the operators applied to each map object at each scale transition to mitigate the effects of generalization operator imbalance. An operator-balanced dataset improves not only the capacity of trained DL models to map generalization but also the design of appropriate DL architectures.
<figure><img src="image_17.png" /><figcaption>**Figure 2.** Prevalence of generalization operators at different scales in swisstopo maps (Senn 2025, p. 7), inspired by the information given in Spiess *et al.* (2005) and the illustration of (Cecconi *et al.* 2002, Fig. 3).</figcaption></figure>

Although a (semi-)automatic generalization operator annotation method seems feasible to develop a multiscale map generalization dataset (Fu *et al.* 2023, Senn *et al.* 2024), the quality of datasets for DL remains challenging to control. Many highly abstract operators (e.g., typification) may be hard to formalize through a (semi-)automatic annotation method. Moreover, different operators may be applied to similar generalization scenarios due to human cartographers’ subjective choices and different evolving versions of maps, prone to impacting the effectiveness of DL-based map generalization models. This implies that learning mechanisms also have to deal with label noise. One possible direction to tackle this challenge is to document this ambiguity of the operator application in the metadata of the dataset, which can enable expert cartographers, users, or other agents to purposely double-check these uncertain cases and further improve the quality of datasets (Weibel 1991, Stoter *et al.* 2009).

## 3.2. Methodological innovation

### 3.2.1. Customizing cartographic generalization loss

As a key component of a DL architecture, the loss function evaluates the performance of trained DL models in training datasets and guides the optimization of the hyper-parameters of the models. Previous studies mainly applied various existing DL architectures in an unmodified fashion to generalize maps. As shown in Table 1, for raster map DL models, mean squared error (MSE) and binary cross-entropy (BCE) have been frequently used in loss functions (Feng *et al.* 2019, Courtial *et al.* 2020, Yu and Chen 2022, Jiang *et al.* 2023). These metrics focus solely on the prediction performance of each pixel in a raster map and treat each pixel equally and independently, but neglect to evaluate the generalized map objects (e.g., buildings, roads) as a whole, including their spatial relationships across scales, which is far removed from the cartographic principles of map generalization (Fu *et al.* 2024a).
<figure><img src="image_18.png" /><figcaption>**Table 1.** Classification of loss functions for learning map generalization.</figcaption></figure>

<table><thead><tr><th>Loss type</th><th>Loss function</th><th>Applied data model</th><th>Target procedure</th></tr></thead><tbody><tr><td rowspan="2">Distance</td><td rowspan="2">Mean squared error</td><td>Raster</td><td>End-to-end learning (Feng et al. 2019, Yu and Chen 2022)</td></tr><tr><td>Vector</td><td>DL-based operator implementation: simplification (Zhou et al. 2023)</td></tr><tr><td rowspan="2">Distribution</td><td rowspan="2">Binary cross entropy</td><td>Raster</td><td>End-to-end learning (Courtial et al. 2020)</td></tr><tr><td>Vector</td><td>Cartographic feature engineering (Yan et al. 2021, Knura 2024)</td></tr><tr><td rowspan="3">Geometry</td><td>Direction invariant</td><td>Raster</td><td>End-to-end learning (Kang et al. 2020)</td></tr><tr><td>Boundary</td><td>Raster</td><td>End-to-end learning (Zhou et al. 2024)</td></tr><tr><td>Shape, area, and angle</td><td>Vector</td><td>DL-based operator implementation: simplification (Yan and Yang 2024).</td></tr><tr><td>Topology</td><td>Topological connectivity</td><td>Raster</td><td>End-to-end learning (Courtial et al. 2023)</td></tr></tbody></table>

Hence, a natural opportunity for GeoAI in map generalization is to customize cartographic generalization loss functions to improve the capacity of DL models in transforming the geometry of map objects across scales. Several attempts have been made in this respect. Kang et al. (2020) incorporated a geometric consistency loss in the loss function of a cycle generative adversarial network (CycleGAN) for building generalization. However, the empirical results show that the proposed loss performs poorly in producing orthogonal sides of buildings at target scales. Courtial et al. (2023) proposed a topological connectivity loss function to learn the generalization of mountain roads in raster maps. Yan and Yang (2024) considered shapes, areas, and angles in the loss function to simplify polylines in vector maps. Despite the conceptual novelty, the results were not validated with maps generalized by cartographers, and the feasibility of the designed loss function thus needs to be further investigated.

Given the abundant rules and constraints imposed on traditional map generalization (Balley et al. 2014), it is reasonable to customize more loss functions specialized for incorporating cartographic knowledge for DL models to learn map generalization, improving the capacity of GeoAI models to capture spatial patterns at certain scales and cross-scale transformations of geographic phenomena. For instance, loss functions could be derived from six main categories of constraints, including position, topology, shape, structural, functional, and legibility (Beard 1991, Harrie and Weibel 2007). While various metrics have been developed to quantify the first four constraints, such as positional accuracy, area, and higher-order properties such as connectivity, density, or sinuosity (McMaster and Shea 1992, Steiniger and Weibel 2007, Mackaness and Ruas 2007), the latter two, that is, functional and legibility constraints, are more strongly related to map purpose and thus harder to formalize (Stigmar and Harrie 2011).

### 3.2.2. Contextualizing the learning of map generalization

The core of map generalization is to represent and emphasize different geographic phenomena at different scales. Ignoring the geometric, semantic, and topological relationships among map objects renders it impossible to generalize individual objects, let alone groups of map objects (Regnauld and McMaster 2007). However, existing DL studies are typically limited to the generalization of a single class of map objects (e.g., buildings or roads) but do not allow for the interplay of different classes of map objects (e.g., a road relative to its surrounding buildings) to be considered, enabling
<figure><img src="image_19.png" /></figure>

the inclusion of spatial context in map generalization. To achieve end-to-end learning for raster map generalization, an intuitive solution to the integration of spatial context could be to represent different classes of map objects in different channels, as was done by Courtial *et al.* (2022). Alternatively, for learning vector map generalization, heterogeneous graph learning could be a feasible direction (Ektefaie *et al.* 2023). Additionally, few studies have included natural map objects as context in learning map generalization. Beyond artificial features (e.g., buildings and road networks), naturally occurring features, such as water bodies, river networks, vegetation areas, and coastlines (Steiniger and Weibel 2007, Liu *et al.* 2011, Roth *et al.* 2011), can impose significant constraints on generalizing other map object classes due to their distinct functions of shaping geographic landscapes. This imposes further necessity for future DL models to be able to represent interactions between different map object classes and thus learn more reasonable generalizations.

### 3.2.3. Multimodal learning for cartographic feature engineering

In digital maps, each object can be represented by the geometry and semantics of its corresponding geographical features or phenomena. Hence, they can have multiple data modalities. Specifically, the geometry of map objects can be classically encoded with either raster or vector data structures, while the semantics are represented by textual or numerical attributes attached to the corresponding geographical features. Moreover, spatial relations (e.g., proximity, topology, direction) between map objects are complex, and they are typically explicitly encoded with graph/network structures (Burghardt and Cecconi 2007) for conventional map generalization.

These different data modalities offer complementary information for map generalization. For instance, vector data is concise in characterizing the shapes of map objects, while raster data facilitates spatial computing with simple and regular grids. However, existing map generalization DL approaches only allow for a single modality of map objects, either raster (Feng *et al.* 2019, Courtial *et al.* 2023, Fu *et al.* 2024a) or vector (Zhou *et al.* 2023, Yan and Yang 2024). Thus, we recommend introducing multimodal learning (Baltrušaitis *et al.* 2018) to fully use the features of different modalities for map generalization (Lafon *et al.* 2023). An essential opportunity highlighting GeoAI is the fusion of information from different modalities of map data (e.g., raster, vector, textual) to predict generalized maps in DL. As Baltrušaitis *et al.* (2018) summarized, the fusion can take place in the *early*, *late*, and *both* (i.e., hybrid) stages in a deep learning procedure. Specifically, as illustrated in Figure 3, early fusion refers to fusing embedding features extracted from unimodal representation learning models, for instance, convolutional neural networks for raster (Mc Cutchan and Giannopoulos 2022) and graph neural networks for vector and network data structures (Mai *et al.* 2022, 2023, Yu *et al.* 2024). Such a fusion is particularly suitable for end-to-end learning of map generalization. In contrast, late fusion utilizes intermediate predictions from unimodal data and further integrates them to improve the decision on the final map generalization. As shown in Figure 4(a), late fusion mainly suits pipeline-based learning to assist the operator type selection and process modeling. As a third option, hybrid fusion integrates the outputs of both early fusion
<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /><figcaption>**Figure 3.** Graphical illustration of early fusion in multimodal representation learning. Given a map, which can be represented with four data modalities, including raster, vector, graph, and attribute, different encoders can be applied to extract feature embeddings. Then, these different modalities of feature embeddings can be concatenated as an overall feature embedding, which then provides the input for the prediction of the generalized map at the target scale.</figcaption></figure>

and late fusion as a new embedding to predict generalized maps, as illustrated in Figure 4(b).

### 3.2.4. Integrated solutions

Given varying generalization levels of individual or groups of map objects, it is challenging to train a 'one-size-fits-all' model to achieve complete map generalization across scales, while an integrated solution including multiple locally optimal solutions is more feasible. In raster map generalization, the satisfying performance of different generalization operators typically exists in different training iterations (Winkler 2023). Also, different DL architectures yield varying generalization performance with different classes of map objects (Feng et al. 2019, Zhou et al. 2024). Hence, it is necessary to dig into the map generalization learning process by analyzing the iterations of trained DL models to understand the associations between the characteristics of map objects and the generalization performance of certain DL architectures. As shown in Figure 5, the intermediate outcomes of training a DL model for map generalization at multiple iterations can be integrated to form a final, generalized map. Concerning the end-to-end
<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /><figcaption>**Figure 4.** Graphical illustration of late fusion and hybrid fusion in multimodal representation learning. (a) In the late fusion approach, unimodal data will be directly input to a predictor to output intermediate map generalization results, such as operators to be applied to certain map objects, which will be subsequently integrated as a final decision. (b) Hybrid fusion integrates the separate outputs of both early fusion and late fusion into a new embedding, which then serves as input for the prediction of the generalized map.</figcaption></figure>

learning solution, iterative deep learning (Heffernan et al. 2015, Chen et al. 2023) is worth being investigated further for the case of generalizing maps.

In terms of vector map generalization through pipeline-based learning, the challenging part still lies in the process modeling following the implementations of generalization operators for individual map objects. An alternative solution is to tackle different operators as competing agents or tasks in parallel and use agent-based modeling approaches or multi-task learning paradigms (Zhou et al. 2023) to achieve the integrated solution of DL-based vector map generalization. Although agent-based modeling (ABM) is not new in automatic map generalization (Galanda and Weibel 2002, Duchêne 2003, Ruas and Duchêne 2007), the recent advances in ABM, such as
<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /><figcaption>**Figure 5.** Graphical illustration of an example integrated solution for raster map generalization. During the iterative training of a DL model for map generalization, different epochs (i.e., iterations) may produce partially satisfying results. Hence, we can integrate the results at different epochs into a complete, generalized map at the target scale.</figcaption></figure>

deep reinforcement learning (Arulkumaran et al. 2017) and inverse reinforcement learning (Arora and Doshi 2021), have great potential to improve the automation of coherent map generalization. For instance, a map object can be modeled as an agent and individual generalization operators as actions. The reward function, such as the legibility of a generalized map, can thus be designed to optimize the joint work of different operator actions.

### 3.2.5. Cartographic knowledge transfer

Advanced DL models are promising to learn consistent cartographic knowledge in generalization. However, transferring learned cartographic knowledge from a given dataset to another unknown dataset is challenging. In a DL paradigm, the cartographic knowledge is implicitly acquired and thus difficult to formalize and cast into explicit generalization rules with associated parameters (Weibel et al. 1995, Mustière 2005). Fu et al. (2024b) recently introduced explainable AI methods and reasoned what shape transformation knowledge is learned by the U-Net model across scales in image maps. However, it still remains unclear under what conditions specific generalization operators should be applied, hampering the generation of transferable and applicable generalization rules. Beyond formalizing rules from DL models and transferring them to other data, another more straightforward solution is transfer learning (Ma et al. 2024). Similar to traditional map generalization, where the process is learned by cartographic novices based on a typically small set of examples given by expert cartographers, pretrained models are expected to be able to predict the generalized maps with few new examples and even without seeing new examples (Wang et al. 2021).

### 3.2.6. Large language models for map generalization

The recent versions of large language models (LLMs) such as ChatGPT, Gemini, Copilot, or Claude are proving effective for various tasks. In medical science, for instance, LLMs sometimes perform better than specialized models (Nori et al. 2023). However, this performance is not reproduced for any specific domain (Xu et al. 2025b), and it is thus interesting to discuss how LLMs might perform on cartographic
generalization problems. In the cartography domain, LLMs so far have been employed to generate different styles of maps (Wang et al. 2025) and thematic maps (Zhang et al. 2024), but their applications to map generalization are rare. The scope of this section is not to fully analyze the capabilities of LLMs in terms of map generalization, but to show why future research on map generalization might consider LLMs as a promising direction.

To this end, we carried out an attempt to address map generalization problems with ChatGPT-5 based on small image patches containing building footprints. Figure 6 shows a small image containing 4 building footprints from an open topographic dataset (Feng et al. 2019, Fu et al. 2024a). We used the prompt shown in Figure 6, without prompt engineering, that is, this is the first prompt we tried. The quality of the output image is very good, with adequate generalization choices and no "wobbly-wall effect" (Fu et al. 2024a). With other similar images of detailed buildings, ChatGPT was able to reproduce good generalization results. However, when we asked the model how this generalization was performed, the answer did not explain how the LLM solved the task. Still, it showed that the problem was understood, as a solution based on Python libraries or GIS tools was proposed to achieve similar results, using a sequence of Douglas & Peucker line simplification, morphological operations, and then line smoothing. Additionally, it should be noted that generalizations by ChatGPT are stochastic, as we observed that asking several times to generalize the same results does not yield the exact same results, though all can be considered as correct. Overall, these promising first results suggest that even for very specific tasks, such as cartographic generalization, current LLMs may compete with the specialized models discussed in other parts of this paper.

Furthermore, given that plenty of cartographic knowledge created by expert cartographers exists in different forms, such as map specifications and constraints (Balley et al. 2014), we highly advocate the use of the retrieval-augmented generation (RAG) technique (Yuan et al. 2025, Yu et al. 2025b) to generate trustworthy maps at multiple scales. Compared to LLMs only, RAGs would allow the retrieval of critical map generalization processes and operators through prompting before LLMs start to formally perform the generalization on maps. We also observe the Model Context Protocol (MCP)

<figure><img src="image_27.png" /></figure>

This image shows a map with highly detailed buildings.
Can you provide me with a similar image containing these
buildings after cartographic generalization, i.e., removing
the small details on the outlines of the buildings?

<caption>**Figure 6.** An image of detailed building footprints generalized by ChatGPT-5 based on the given prompt.</caption>
<figure><img src="image_28.png" /></figure>

as an emerging interface bridging AI agents and existing tools, which has been successfully applied to certain GeoAI tasks (Xu et al. 2025a) and is also proposed as a future direction by the GeoAI community (Li et al. 2025, Yu et al. 2025a). Following the rapid advance of LLMs, RAG, and MCP techniques, we expect the development of LLM-based agents dedicated to this topic in the very near future. Notably, although LLMs are arguably a highly promising direction towards fully automated map generalization, expert cartographers must be closely involved in the loop of producing multi-scale maps by LLMs to guarantee the quality of maps due to potential LLM hallucinations (Massenon et al. 2025). This thus links to the evaluation of map generalization, which will be addressed in Section 3.4.

## 3.3. Broader generalization tasks

### 3.3.1. Small-scale map generalization

Maps at different scales convey different patterns and relationships among geographic features (Mackaness et al. 2014). Hence, each scale of the map has its unique usage for a certain purpose and cannot be easily interchanged. For example, planning communities and neighborhoods typically requires maps at the scale of 1:50,000 or larger, while smaller scales such as 1:100,000 are preferable for planning regions containing multiple municipalities. To date, map generalization using DL is focused on scale transitions from large to medium scales, ranging from 1:5k to 1:50k. At such scales, generalization mainly involves selection, simplification, and aggregation, where the degree of abstraction level of maps is mild and thus is more readily learned through DL models. In contrast, a more intensive abstraction takes place at the transitions from medium scales to small scales, as the geometric shapes of map objects and the spatial layout of the overall map may change dramatically. It is worth investigating to what extent existing advanced DL models can, and how DL models should be, adapted to learn generalizing maps to small scales.

### 3.3.2. Thematic map generalization

Beyond topographic maps that represent universal geographic features, such as built-up areas, roads, water bodies, and vegetation areas, there are many thematic maps (e.g., transportation maps, geological maps) aimed at more specific domains and tasks (Balley et al. 2014, Sester et al. 2014). In contrast to the generalization of topographic maps in which locational constraints and functional constraints are jointly applied (Klippel et al. 2005), thematic map generalization essentially focuses on preserving functions and relations among task-related map objects, while abstracting locational details with different generalization logics (Steiniger and Weibel 2007, Roth et al. 2011). For example, the geometric shapes of the polygonal elements in vegetation maps or geological maps are typically not as regular as those of building footprints in topographic maps, and in many cases, small shapes may have to be enlarged instead of being removed, given certain contexts, such as small deposits of important minerals depicted in geological maps (Sayidov et al. 2020).

While GeoAI has been rather frequently applied to topographic map generalization research and even to the creation of realistic multi-scale map products by national
<figure><img src="image_29.png" /></figure>

mapping agencies (Usery et al. 2022, Jaroenchai et al. 2024), research on thematic map generalization using GeoAI has received much less attention. To date, DL methods for generalizing feature classes that may also occur on thematic maps have been dedicated to the selection and simplification of linear thematic features, e.g., mountain roads (Courtial et al. 2023), river networks (Du et al. 2022b, Yu et al. 2023), DEM contour lines (Yu and Chen 2022), and coastlines (Jiang et al. 2023) by formulating them as a classification task. Despite satisfying classification results, they rarely control for the inadvertent introduction of topological errors by selection or simplification of linear thematic features, a critical characteristic in maintaining the connectedness of linear networks, such as transportation or river networks. This calls for the development of GeoAI approaches that can perform topology-aware linear thematic map generalization.

Moreover, deep learning has not yet been extensively introduced to generalize polygonal thematic features, particularly naturally occurring map objects, such as lakes, forests, and geological formations (Müller and Zeshen 1992, Regnauld and McMaster 2007, Sayidov et al. 2020). Although their underlying geometric structure may be the same as that of geographic features in topographic maps, both being polygonal features, the logic of generalization is very different, and the involved generalization operators and procedures are also different. For instance, when generalizing geological maps, the key principle is to preserve and represent the evolution of various geological resources and hazards in a long history (Graymer et al. 2006). Therefore, different ages of rocks and different sedimentary layers should be coherently generalized to clearly distinguish their temporal progression by more advanced operators such as aggregation and typification existing in traditional generalization approaches, even though their areas may sometimes be very small (Smirnoff et al. 2012, Zhang et al. 2016, Sayidov et al. 2020). Given such domain-dependent priorities and criteria for thematic mapping, it is necessary to also investigate the capacity of DL in thematic cartography, such as the generalization of polygonal maps.

### 3.3.3. Conceptual generalization

Map generalization induces both geometric (or graphical) and conceptual changes to a map or database. This paper has dealt primarily with the former process, that is, graphical generalization, as Spiess et al. (2005) call it. A look at commonly used typologies of generalization operators also reveals far more operators for geometric/graphical changes than conceptual (or attribute) changes. Regnauld and McMaster (2007) list only (re-)classification, symbolization, and a set of operators on the schema of a database. Yet, operators of conceptual generalization typically may induce more far-reaching changes than their graphical counterparts. For instance, changing the classification system of a land cover map may potentially affect all polygons represented on that map, as may changes to the schema underlying a map database. Fortunately, conceptual generalization operators can often follow stringent rule systems, such as a classification system used for soil maps, or a schema that has been designed for a large corporate infrastructure database, making them more 'predictable' and thus easier to implement. In cases where such rule systems do not exist, however, the conceptual modeling tasks of abstraction and summarization are needed. After all, one of the key tasks of map generalization is the abstraction of spatial reality in the form of
<figure><img src="image_30.png" /></figure>

maps (Brassel and Weibel 1988, p. 230), clearly a non-trivial task. Given that LLMs are starting to reach performance levels of human experts in summarization (Laban et al. 2023) and are also improving their capacities in reasoning abstraction (Qu et al. 2025), the use of LLMs in conceptual generalization offers a promising avenue for future research.

### 3.4. Multi-level evaluation

The evaluation of generalized maps focuses on the optimization levels of cartographic information regarding the symbolization, abstraction, and aesthetics at a given scale (Bard 2004, Mackaness and Ruas 2007). It holds varying criteria at three levels: micro, meso, and macro-level. As detailed in Table 2, the micro-level evaluations focus on the quality of individual map features regarding the dimensions of position, orientation, size, granularity, and shape. The meso-level evaluations target the quality of a group of neighboring map features (e.g., the map objects contained in a street block) in representing spatial patterns at certain scales and measure their difference in dimensions of topological relationships, spatial arrangement, and density. Instead of quality, the macro-level evaluations emphasize the quantity of map features, including the number and proportion of map features, total area, and total length.

Evaluating the quality of generalized maps at multiple levels is a challenging task even for expert cartographers due to the large variety of map types, changing user requirements, and the fact that map generalization is in many ways an ill-defined problem that allows multiple satisfactory solutions. Although some automated evaluation methods have been developed (Bard 2004, Stoter et al. 2009, Zhang et al. 2013), they rarely allow the evaluation procedure to adapt to user contexts and map use tasks, which drive the change of evaluation criteria at different levels. One notable exception is agent-based approaches (Barrault et al. 2001, Ruas and Duchêne 2007), where agents representing micro- and meso-level map objects have a self-evaluation capacity, activating generalization operations depending on their own state and contextual factors. Currently, the most promising solution appears to be to introduce LLMs into comprehensive evaluations of generalized maps, investigating the alignment of LLMs with cartographic communication at multiple scales (Wang et al. 2023b), while LLMs are promising to generalize maps at different scales as discussed above. For example, we may investigate the consistency of judgment between LLMs and professional cartographers in understanding qualitative cartographic evaluation metrics

<table><caption>**Table 2.** Quantitative evaluation dimensions and metrics.</caption><thead><tr><th>Level</th><th>Dimension</th><th>Example metric</th></tr></thead><tbody><tr><td rowspan="5">Micro-level</td><td>Position</td><td>Position deviation</td></tr><tr><td>Orientation</td><td>Orientation deviation</td></tr><tr><td>Size</td><td>Area, length, width</td></tr><tr><td>Granularity</td><td>Size of the smallest shape</td></tr><tr><td>Shape</td><td>Elongation, convexity</td></tr><tr><td rowspan="3">Meso-level</td><td>Geometry</td><td>Relative distance</td></tr><tr><td>Topology</td><td>Intersection, touching, inclusion</td></tr><tr><td>Spatial arrangement</td><td>Density, cluster, free space, alignment, symmetry</td></tr><tr><td rowspan="2">Macro-level</td><td>Map object quantity</td><td>Object count, object count reduction factor</td></tr><tr><td>Map object geometry</td><td>(Proportion of) total area covered, total length</td></tr></tbody></table>
<figure><img src="image_31.png" /></figure>

relating to map legibility, allowing LLMs to select and rank the importance of these
metrics in different map tasks. Following the empirical investigation of Wang et al.
RAG can still be employed to retrieve the relevant evaluation dimensions of general-
ized maps from expert cartographic knowledge based and then assess the visual
quantity and quality of generalized maps. Another interesting approach may be to
couple LLMs and the above-mentioned multi-agent systems, as done by Wang et al.
(2025) for the task of map style transfer. Notably, there are usually multiple alternative
map generalization solutions that satisfy user needs and map specifications for a given
generalization problem. The evaluation procedure should thus be able to accommo-
date alternative solutions of generalized maps, following the notion of multiple repre-
sentations (Mustière and van Smaalen 2007).

4. Beyond map generalization: interdisciplinary GeoAI research

GeoAI-driven map generalization techniques have great potential, particularly in the age of geospatial big data, to process and manage more forms of spatial data, beyond 2-D maps. Furthermore, the nature of learning map generalization, which lies in learning fine-grained but critical geometric transformations, can be interconnected to tasks in other fields, such as remote sensing. Therefore, an interdisciplinary GeoAI research agenda can be derived from current experience in map generalization.

4.1. Foundation models

The recent surge of ‘foundation models’ has been powered by pre-training on a vast amount of texts and images, yet these models still perform poorly with vector-based geographic data such as points, polylines, and polygons (Mai et al. 2024). They could encode complex geometry properly, but struggle to reconstruct shapes without topological errors, and to modify geometries with fine-grained control (Janowicz et al. 2025). By contrast, map generalization offers a mature and well-documented set of geometric operations, such as simplification, smoothing, aggregation, displacement, and typification, backed by decades of quality standards. Treating map generalization data as a training playground could provide foundation models with precisely the kind of supervised knowledge they lack for spatial reasoning. Furthermore, given the nature of map generalization in representing varying geographic phenomena with scales, the research on map generalization with AI can foster the development of scale-dependent geo-foundation models.

4.2. 3-D City model management in digital twins

Nowadays, 3-D city models are increasingly and rapidly developed as a critical compo-
nent of digital twins, supporting various smart city applications, such as urban plan-
ning (Schrotter and Hürzeler 2020, Lehner and Dorffner 2020, Cureton and Hartley
2023). High-fidelity 3-D city models digitalize as many details as possible for realistic
physical city environments, but such heavy models do not lend themselves to efficient
management, analysis, and visualization, which calls for the generalization of 3-D city
models (Burghardt et al. 2014, Billen et al. 2014, Shen et al. 2025). Although the
<figure><img src="image_32.png" /></figure>

CityGML data standard offers a technical generalization solution by allowing to define different levels of detail (LoDs) based on semantic elements of city models (Gröger and Plümer 2012), the specific generalization method to derive each LoD model is beyond the scope of the standard (Thiemann 2002, Meng and Forberg 2007, Fan and Meng 2012). GeoAI methods for 2-D map generalization are promising to be extended to support 3-D city model generalization (Wu et al. 2019). Moreover, the latest progress on 3-D geometric deep learning may also inspire the development of GeoAI models (Guo et al. 2021).

### 4.3. Movement data management and mining

Movement data offer a way of recording the trajectories of humans, animals, and many other agents or moving objects (Miller et al. 2019, Dodge et al. 2020, Pappalardo et al. 2023). Managing movement data is a challenging problem due to its typical characteristics of big data concerning volume, velocity, and variety (Wang et al. 2022). Map generalization significantly reduces the volume of spatial data while preserving the key spatial information. Thus, it has been applied to manage massive data, enabling functions such as efficient visualization (Andrienko and Andrienko 2010, Fu et al. 2021) and privacy protection (Nergiz et al. 2008, Komishani and Abadi 2012). Deep learning is bringing movement data management to a new level (Chen et al. 2024), while GeoAI-based map generalization methods would also be promising to be applied to movement data management. In return, new DL models originating from the movement data field may further foster new GeoAI models for map generalization, particularly for generalizing polyline features (Rao et al. 2020).

### 4.4. Temporal change detection in maps

The essence of map generalization is to make subtle but visually meaningful changes to map objects across scales. The core task where the DL models are involved is to learn such changes, to which detecting and modeling changes in maps over time is analogous. Hence, DL models for map generalization, particularly in raster maps, could be promising to be applied to broader map change detection tasks, such as in land use and land cover maps (Liu et al. 2021, Wang et al. 2022, Wu et al. 2023), historical map archives (Uhl et al. 2018, Chiang et al. 2023, Yuan and Sester 2025), or high-definition maps for autonomous driving (Kim et al. 2021, Bateman et al. 2024, Hui et al. 2024).

## 5. Discussion

When comparing the results achieved by current DL-based models for map generalization with those of state-of-the-art conventional, algorithm-based solutions, we have to admit that the latter still clearly outperform DL solutions in terms of cartographic quality. However, for a fair assessment of these obvious differences, we need to take several factors into account.
<figure><img src="image_33.png" /></figure>

First of all, a simple fact: DL in map generalization has only been around for slightly more than five years, while the overall history of research in automated map generalization spans roughly sixty years (Weibel and Buttenfield 1992, Weibel and Dutton 1999).

Second, what we see today with the advent of GeoAI approaches in cartography and map generalization is, without overstatement, a paradigm shift in how map generalization is fundamentally approached. Above all, DL can learn from examples (i.e., training data) rather than having to rely on explicit knowledge formalization, which has been a severely limiting factor to the further advancement of conventional approaches since roughly the mid-2000s. As a consequence, we can expect future Al-based map generalization approaches to be easier to use than the conventional algorithm- and rule-based approaches.

Third, it is important to understand that by using DL models, we are not simply extending the state-of-the-art algorithmic methods, but in fact fundamentally reformulating the way map generalization is accomplished. In other words, we are restarting from scratch, rather than incrementally enhancing existing methods.

Fourth, the situation today can be likened to the early to mid-1990s. With the seminal paper of Beard (1991), a fundamentally new conceptual framework was introduced that led to the development of constraint-based methods in map generalization, which, for reasons explained in Harrie and Weibel (2007), made the possible solutions much more flexible and opened up new opportunities. The advent of constraint-based methods also meant a paradigm shift, but as with DL models today, this paradigm shift took time to take effect and give rise to novel solutions relying on constraints, such as optimization and agent-based models (Ruas and Plazanet 1996, Barrault et al. 2001, Duchène 2003, Bader et al. 2005, Sester 2005, Ruas and Duchène 2007).

So, when comparing the cartographic quality of DL solutions with state-of-the-art algorithmic and rule-based methods, we are essentially comparing the results of an evolving technology with the results of a rather mature technology (which, however, still has not achieved full automation and still requires substantial manual edits in production workflows). We are convinced that this new avenue should be further explored, despite initial difficulties. With this paper, we hope to contribute to providing guidance to those who would like to embark with us on this journey.

## 6. Conclusion

Map generalization research addresses a problem of fundamental importance for the spatial sciences, focusing on the automated multi-scale representation and communication of geographic information in the form of maps. In the last few years, GeoAI has offered a new research paradigm for automated map generalization, which is sparking renewed attention and research activity in a domain that has been experiencing declining research interest in the 2010s.

In this foresight paper, we started off from a review of the evolution of existing GeoAI methods for map generalization, before developing a research agenda for this field to foster its theoretical and methodological development. We identified the urgent need to build high-quality datasets for learning map generalization as a
<figure><img src="image_34.png" /></figure>

foundation for this work. Given the current focus of GeoAI methods on the application of DL models with limited adaptation, we emphasized the need for efforts to be devoted to cartographic feature engineering (e.g. contextualization of learning and multimodal learning), optimization processes (e.g., customization of cartographic generalization loss), making full use of learning outcomes (e.g., integration of intermediate learning results and cartographic knowledge transfer across datasets), and further pursuing the latest AI techniques, such as LLMs.

Finally, since the principles and methods of map generalization essentially underpin a methodology for representing multi-scale spatial data that is essential in virtually all domains dealing with spatial data, we also encourage the transfer of these principles and GeoAI methods to manage new forms of spatial data and geospatial tasks. These include developing GeoFoundation models, managing 3-D city models in digital twins, managing and mining movement data, and detecting temporal changes in maps. Given the rapid advancement of AI tools and models, including LLMs and RAG, we are confident that fully automated, end-to-end solutions are within reach. Similarly, GeoAI will reach a new level when it can fully automate map generalization at multiple scales, mimicking the way humans process multi-scale spatial data.

## Disclosure statement

No potential conflict of interest was reported by the author(s).

## Funding

Rober Weibel, Cheng Fu, and Zhiyong Zhou acknowledge support of this research by the Swiss National Science Foundation (SNSF) through project number 200021_204081 DeepGeneralization. Zhiyong Zhou acknowledges the Postdoc.Mobility Fellowship [No. 235381] funded by SNSF in partially supporting this research. Cheng Fu acknowledges support from the China Agricultural University Research Funding for High-level Talents [No. 2025RC056]. Yu Feng acknowledges support by the German Research Foundation (DFG) through project OSMSim [No. 499168241]. Guillaume Touya acknowledges support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [grant agreement No. 101003012, LostInZoom].

## Notes on contributors

Zhiyong Zhou is an SNSF postdoctoral research fellow at the Department of Geography, University of Wisconsin-Madison. Before this position, he was a postdoc at the Department of Geography, University of Zurich, from which he obtained a Ph.D. in Geography/Earth System Science in 2022. His research interests include GeoAI, location-based services (LBS), computational cartography, and urban analytics. He contributed to the writing of the original draft, conceptualization, methodology, investigation, and validation.

Cheng Fu is a professor at China Agricultural University. His research interests include place modeling, human mobility, and map generalization. He contributed to review, editing, conceptualization, methodology, investigation, supervision, and funding acquisition.

Yu Feng is a professor at Hochschule Mainz – University of Applied Sciences. Prior to his current position, he worked as a group leader and postdoctoral researcher at the Technical University of Munich (TUM). Earlier in his career, he was a Research Associate at Leibniz University Hannover,
<figure><img src="image_35.png" /></figure>

where he also completed his PhD. His research interests include GeoAI, volunteered geographic information (VGI), and cartographic generalization. He contributed to review, editing, conceptualization, methodology, and investigation.

**Guillaume Touya** is a senior researcher, at IGN France (the French mapping agency) and Univ Gustave Eiffel, holding a PhD and habilitation in GI science from Paris-Est University. His research interests focus on automated cartography, map generalisation and volunteered geographic information, with a particular interest in research approaches to multi-scale cartography that mix automated cartography, spatial cognition and human-computer interaction issues. He is the principal investigator of the recent LostInZoom project, funded by the Europe Research Council (ERC) and the chair of the ICA (International Cartographic Association) commission on map generalisation and multiple representation. He contributed to review, editing, conceptualization, methodology, and investigation.

**Monika Sester** is a surveying engineer by training (University Karlsruhe) and earned her PhD on a topic of Machine Learning at the University of Stuttgart in 1995, and her habilitation in 2000 on the automatic generation of multiple representations of geodata. Since 2000 she has been professor and head of the Institute of Cartography and Geoinformatics (ikg) at Leibniz University Hannover. She and her group work on the automation of spatial data processing with methods from computational geometry, optimization and Al. She contributed to review, editing, conceptualization, methodology, and investigation.

**Robert Weibel** is a Professor Emeritus of Geographic Information Science at the University of Zurich, Switzerland. He is interested in computational cartography; mobility analytics with applications in health, transportation and animal ecology; and spatial analysis and modeling for the study of language evolution. From 1992 he built up the ICA Commission on Map Generalization and served as Commission Chair until 2003. He contributed to reviewing, editing, conceptualization, methodology, investigation, supervision, funding acquisition, and project administration.

## ORCID

Zhiyong Zhou http://orcid.org/0000-0002-6117-2071

Cheng Fu http://orcid.org/0000-0003-1562-7941

Yu Feng http://orcid.org/0000-0001-5110-5564

Guillaume Touya http://orcid.org/0000-0001-6113-6903

Monika Sester http://orcid.org/0000-0002-6656-8809

Robert Weibel http://orcid.org/0000-0002-2425-0077

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