A Deep Learning and Ensemble Decision Method to Identify the Urban–Rural Fringe: A Case Study in Kunming City, China

The urban-rural fringe (URF) has become the most dynamic area of land use transition and urban-rural factor flows during urbanization, forming a critical focus of sustainable land management. However, existing identification methods have not adequately captured the fine-scale textures and ambiguous transitional boundaries characterizing the URF. Taking Kunming City as the study region, this study developed a lightweight convolutional neural network (UF-Net) to extract spatial textures and boundary features, integrating it with eXtreme Gradient Boosting to construct a hybrid recognition framework. Multisource remote sensing and geospatial datasets were employed to delineate the URF from 2013 to 2023, and stage-specific driving mechanisms were examined using propensity score matching and binary logit models. The results showed that our framework achieved an overall accuracy of approximately 94% for both periods. Over the decade, built-up areas expanded markedly, and the spatial structure evolved from a single-core pattern characterized by fragmented peripheral development to a polycentric configuration with increasingly continuous URF zones. Chenggong and southern Guandu District emerged as major growth frontiers, while URF morphology shifted from linear to ring-shaped and cluster-type forms. Furthermore, the drivers of urban expansion transitioned from dominance by natural terrain and ecological suitability to a regime shaped primarily by human activities and transport accessibility. The proposed hybrid recognition framework, integrating deep feature extraction with ensemble-based classification, establishes a generalizable methodological path for interpreting URF evolution, providing analytical support for optimizing urban spatial structure and sustainable development strategies.