气候变化领域集成服务门户(CCPortal): Detecting global urban expansion over the last three decades using a fully convolutional network

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DOI	10.1088/1748-9326/aaf936
	Detecting global urban expansion over the last three decades using a fully convolutional network
	He C.; Liu Z.; Gou S.; Zhang Q.; Zhang J.; Xu L.
发表日期	2019
ISSN	17489318
卷号	14 期号:3
英文摘要	The effective detection of global urban expansion is the basis of understanding urban sustainability. We propose a fully convolutional network (FCN) and employ it to detect global urban expansion from 1992-2016. We found that the global urban land area increased from 274.7 thousand km2-621.1 thousand km2, which is an increase of 346.4 thousand km2 and a growth by 1.3 times. The results display a relatively high accuracy with an average kappa index of 0.5, which is 0.3 higher than those of existing global urban expansion datasets. Three major advantages of the proposed FCN contribute to the improved accuracy, including the integration of multi-source remotely sensed data, the combination of features at multiple scales, and the ability to address the lack of training samples for historical urban land. Thus, the proposed FCN has great potential to effectively detect global urban expansion. © 2019 The Author(s). Published by IOP Publishing Ltd.
英文关键词	deep learning; fully convolutional network; global urban expansion; land surface temperature; nighttime light data; vegetation index
语种	英语
scopus关键词	Atmospheric temperature; Convolution; Deep learning; Expansion; Land surface temperature; Convolutional networks; Multiple scale; Night-time lights; Remotely sensed data; Training sample; Urban expansion; Urban sustainability; Vegetation index; Urban growth; accuracy assessment; data set; expansion; land surface; learning; remote sensing; sustainability; urban area; vegetation index
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154672
作者单位	Center for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; Department of Geosciences, Murray State UniversityKY 42071, United States; ESPRE, Beijing Normal University, Beijing, 100875, China; Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; School of Land Science and Technology, China University of Geosciences, Beijing, 100083, China
推荐引用方式 GB/T 7714	He C.,Liu Z.,Gou S.,et al. Detecting global urban expansion over the last three decades using a fully convolutional network[J],2019,14(3).
APA	He C.,Liu Z.,Gou S.,Zhang Q.,Zhang J.,&Xu L..(2019).Detecting global urban expansion over the last three decades using a fully convolutional network.Environmental Research Letters,14(3).
MLA	He C.,et al."Detecting global urban expansion over the last three decades using a fully convolutional network".Environmental Research Letters 14.3(2019).