## 1 Introduction  
Over the past few decades, most cities worldwide have experienced a rapid expansion with unprecedented population growth and industrialization [1, 2]. Cities can be attractive to people as they guarantee a convenient and stable quality of life, with various opportunities including employment, education, and culture [3]. As a result, about 55% of the world's population is currently living in urban areas, which only account for less than 1% of the Earth's land area [4, 5].  
A rapid and unplanned urban expansion, however, has also resulted in serious challenges to sustainable development of the cities. Urban sprawl and suburbanization has increased the burden of infrastructure and traffic congestion [6, 7], and a loss of natural environment and open spaces within the cities has reduced carbon sinks and biodiversity [8, 9], while intensifying air pollution and global warming [10, 11].  
In order to maximize the benefits of urbanization while supplementing its adverse effects, it is essential to predict and control the expansion of urban areas. Accordingly, scholars and practitioners in the urban planning field have developed various methods for modeling the urban growth. Remote sensing data, including satellite images, are utilized in a majority of urban growth models, as they contain a wide range of land use/land cover (LULC) information at the same time [12].  
In early studies, the mathematical models, including land-use transportation (LUT) models [13, 14], agent-based models (ABMs) [15, 16], and cellular automata (CA)-based models [17], were widely used in predicting urban expansion. However, as they assumed that urban areas are spatially homogeneous, those models have difficulties in reflecting the socioeconomic and physical variations within the city [18].  
To overcome the limitations of the conventional approach, the machine learning (ML) and artificial intelligence (AI)-based techniques have recently been adopted for urban expansion modeling [19]. The decision tree [20], random forest [21; 22], support vector machine [23; 24], and artificial neural networks [5; 25] are some of the most widely used models in existing research. Although the implementing mechanisms and algorithm structures of each model are different, they have a common strength in developing highly accurate urban growth models by collecting and training large amounts of LULC and physical/social characteristics in the city [26]. However, as prediction accuracy increases, the difficulties in understanding the relationships among variables has been pointed out as one of the limitations of these black-box urban growth models [27].  
More recently, an explainable artificial intelligence (XAI) framework has been highlighted among researchers to overcome the weakness of the aforementioned ML and AI models [28]. While there is no clear definition of the concept yet, the XAI aims to increase interpretability and explainability of AI models [29]. In this regard, XAI models require an additional explainable algorithm, such as Shapley Additive exPlanations (SHAP), to explain how and why an AI model achieves a specific result [30].  
The purpose of the study lies in modeling and predicting the expansion of urban areas in South Korea by utilizing an XAI model. In addition, we aimed at examining the relative importance of several built-environment factors on the urbanization. To this end, the study utilized the land-cover maps in 2007 and 2019, as well as several socioeconomic, physical, and environmental attributes. The following section describes the materials and methods used in the study; then, we summarize and discuss the findings of the study, and conclude with several recommendations for future studies.