数字农科院2.0

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

文献类型： 外文期刊

作者： Chenrui Wang;Xiao Sun;Zhifeng Liu;Lang Xia;Hongxiao Liu;Guangji Fang;Qinghua Liu;Peng Yang

作者机构：

关键词： Convolutional neural network;Deep learning;Urban expansion;Urban-fringe-rural identification;Urban-rural planning

期刊名称： Landscape and Urban Planning

ISSN： 0169-2046

年卷期： 2024 年 249 卷

页码：

收录情况： SCIE（2024版） ; ; SSCI（2024版）

摘要： 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.

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