Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1088/1748-9326/aaf936 |
Detecting global urban expansion over the last three decades using a fully convolutional network | |
He, Chunyang1,2; Liu, Zhifeng1,2; Gou, Siyuan1,2; Zhang, Qiaofeng3; Zhang, Jinshui4,5,6; Xu, Linlin7 | |
2019-03-01 | |
发表期刊 | ENVIRONMENTAL RESEARCH LETTERS |
ISSN | 1748-9326 |
出版年 | 2019 |
卷号 | 14期号:3 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; USA |
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 km(2)-621.1 thousand km(2), which is an increase of 346.4 thousand km(2) 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 ofmulti-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. |
英文关键词 | fully convolutional network global urban expansion deep learning nighttime light data vegetation index land surface temperature |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000460971000002 |
WOS关键词 | LAND ; AREAS ; URBANIZATION ; BIODIVERSITY ; DYNAMICS ; ECOLOGY ; IMAGES ; SCALE |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/36485 |
专题 | 气候变化 |
作者单位 | 1.Beijing Normal Univ, Fac Geog Sci, CHESS, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China; 2.Beijing Normal Univ, Sch Nat Resources, Fac Geog Sci, Beijing 100875, Peoples R China; 3.Murray State Univ, Dept Geosci, Murray, KY 42071 USA; 4.Beijing Normal Univ, ESPRE, Beijing 100875, Peoples R China; 5.Beijing Normal Univ, Fac Geog Sci, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China; 6.Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, Beijing 100875, Peoples R China; 7.China Univ Geosci, Sch Land Sci & Technol, Beijing 100083, Peoples R China |
推荐引用方式 GB/T 7714 | He, Chunyang,Liu, Zhifeng,Gou, Siyuan,et al. Detecting global urban expansion over the last three decades using a fully convolutional network[J]. ENVIRONMENTAL RESEARCH LETTERS,2019,14(3). |
APA | He, Chunyang,Liu, Zhifeng,Gou, Siyuan,Zhang, Qiaofeng,Zhang, Jinshui,&Xu, Linlin.(2019).Detecting global urban expansion over the last three decades using a fully convolutional network.ENVIRONMENTAL RESEARCH LETTERS,14(3). |
MLA | He, Chunyang,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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