## 1 Introduction
Key indicators commonly employed in these analyses include but are not limited to the spatial distances from highly developed or central urban areas [26; 27], populations density [28], and land use patterns [29]. These factors serve as vital components for the demarcation of urban-rural fringes, providing a multidimensional understanding of these complex and evolving zones. Advancements in technology have significantly enriched the analytical toolbox available for urban-rural fringe studies. Notably, geospatial data, including but not limited to Remote Sensing (RS) data, land use data, and Points of Interest (POI) data, etc., have emerged as critical components of spatial analysis. These data types offer distinct advantages in high-precision urban applications [30]. They transcend the limitations imposed by administrative boundaries, providing a more finely grained spatial granularity. Consequently, such geospatial data have gained prominence, increasingly supplanting traditional statistical indicators in the elucidation of spatial development patterns, including the identification of urban-rural fringes [31; 32; 33]. Despite these advances, current methodologies face several critical limitations. Traditional identification methods, often based on unidimensional metrics such as land use, population density, or nighttime light, have limitations in capturing the multi-faceted and dynamic nature of urban-rural fringes. These unidimensional models typically overlook the complex interrelations between urbanization and industrialization, particularly in rural contexts, leading to significant inaccuracies in identifying these transitional zones. Furthermore, although advancements in geospatial technology have enabled more precise analyses, the field still grapples with the constraints of manual threshold settings, static population features, and administrative boundaries. In addition, the unique aspects of human mobility within the urban-rural fringe, a dynamic factor integral to the understanding of urban function and spatial patterns, remain largely untapped. To address these limitations, our study introduces a novel SOE (i.e., scene-object-economy) framework aimed at enhancing the precision and objectivity of urban-rural fringe identification. The SOE framework represents three aspects of characteristics: scene (deep visual features), object (individual elements), and economy (socioeconomic indicators). Unlike traditional models, our approach leverages advancements in geospatial technology and machine learning to construct features from multiple dimensions, including remote sensing imagery, nightlight imagery, POI, and features of buildings. This allows for a more comprehensive, nuanced, and accurate mapping of these complex transitional zones. While our method focuses on identifying the urban-rural fringe, we also recognize the importance of dynamic elements, such as human mobility, in understanding the functioning of these areas. Although not used in the identification process, dynamic population data serve as a separate layer of analysis for exploring the unique population distribution patterns within the identified urban-rural fringe. The remainder of this paper is organized as follows: Section 2 presents the materials and methods, including the study area, data sources, and our SOE framework methodology. Section 3 presents the results of the urban-rural fringe identification and validates them through population distribution patterns. Section 4 discusses the implications of our findings, potential applications, and limitations of our approach. Finally, Section 5 concludes with key findings and future research directions.