A novel full-resolution convolutional neural network for urban-fringe-rural identification: A case study of urban agglomeration region

Abstract

Monitoring urbanization processes is important because they are often accompanied by intensive landscape pattern transitions and pluralistic socioeconomic changes. To effectively monitor urban expansion and support regional planning, it is essential to develop a fast, accurate and universal urban–rural classification model, especially identifying the dynamic spatial patterns of urban, urban–rural fringe and rural areas. Although deep learning can effectively detect land cover changes, its applications in urban–rural identification have received little attention due to a lack of high-quality training datasets. In this study, we develop a novel transferable full-resolution convolutional neural network (FR-Net) to identify urban-fringe-rural areas. A large-scale training dataset was constructed using field surveys and aerial photography, and a data cube was stacked by multiple typical socio-natural indicators. We took the Beijing-Tianjin-Hebei (BTH) urban agglomeration region in China as a case study and identified spatiotemporal evolutions of urban-fringe-rural areas from 2000 to 2020. The results indicated that over the past two decades, the urban–rural fringe expanded outward with urban areas, and both areas gradually increased, with an inverted U-shaped growth rate. Accurate identification of these fringes can benefit regional urban–rural planning and social governance. Based on the identification results, complex socio-ecological impacts of urbanization could be further explored. Testing demonstrated that the developed FR-Net model has high accuracy and robustness. Our developed open-source FR-Net model exhibits transferability and can be applied to multi-scale urbanized areas.