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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
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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). |
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