用于城市-边缘-农村识别的新型全分辨率卷积神经网络:城市群区域案例研究

IF 7.9 1区 环境科学与生态学 Q1 ECOLOGY
Chenrui Wang , Xiao Sun , Zhifeng Liu , Lang Xia , Hongxiao Liu , Guangji Fang , Qinghua Liu , Peng Yang
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引用次数: 0

摘要

监测城市化进程非常重要,因为城市化进程往往伴随着密集的景观格局转换和多元的社会经济变化。为了有效监测城市扩张并支持区域规划,必须开发一种快速、准确和通用的城乡分类模型,尤其是识别城市、城乡边缘和农村地区的动态空间模式。虽然深度学习能有效检测土地覆被变化,但由于缺乏高质量的训练数据集,其在城乡识别中的应用很少受到关注。在本研究中,我们开发了一种新型的可转移全分辨率卷积神经网络(FR-Net),用于识别城市边缘-农村地区。我们利用实地调查和航空摄影构建了一个大规模的训练数据集,并通过多个典型的社会自然指标堆叠了一个数据立方体。我们以中国京津冀(BTH)城市群地区为例,识别了 2000 年至 2020 年城市边缘区与农村地区的时空演变。结果表明,在过去二十年中,城乡边缘地区随城市地区向外扩展,两个地区的面积逐渐增加,增长率呈倒 "U "型。准确识别这些边缘地带有利于区域城乡规划和社会治理。在识别结果的基础上,可以进一步探讨城市化带来的复杂的社会生态影响。测试表明,所开发的 FR-Net 模型具有较高的准确性和鲁棒性。我们开发的开源 FR-Net 模型具有可移植性,可应用于多尺度城市化地区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel full-resolution convolutional neural network for urban-fringe-rural identification: A case study of urban agglomeration region

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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来源期刊
Landscape and Urban Planning
Landscape and Urban Planning 环境科学-生态学
CiteScore
15.20
自引率
6.60%
发文量
232
审稿时长
6 months
期刊介绍: Landscape and Urban Planning is an international journal that aims to enhance our understanding of landscapes and promote sustainable solutions for landscape change. The journal focuses on landscapes as complex social-ecological systems that encompass various spatial and temporal dimensions. These landscapes possess aesthetic, natural, and cultural qualities that are valued by individuals in different ways, leading to actions that alter the landscape. With increasing urbanization and the need for ecological and cultural sensitivity at various scales, a multidisciplinary approach is necessary to comprehend and align social and ecological values for landscape sustainability. The journal believes that combining landscape science with planning and design can yield positive outcomes for both people and nature.
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