Detecting global urban expansion over the last three decades using a fully convolutional network
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 thou...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2019-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/aaf936 |
| _version_ | 1797748040972369920 |
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| author | Chunyang He Zhifeng Liu Siyuan Gou Qiaofeng Zhang Jinshui Zhang Linlin Xu |
| author_facet | Chunyang He Zhifeng Liu Siyuan Gou Qiaofeng Zhang Jinshui Zhang Linlin Xu |
| author_sort | Chunyang He |
| collection | DOAJ |
| description | 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 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. |
| first_indexed | 2024-03-12T16:00:14Z |
| format | Article |
| id | doaj.art-13422b69b20a4b559adb52a0ce26ca4a |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T16:00:14Z |
| publishDate | 2019-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-13422b69b20a4b559adb52a0ce26ca4a2023-08-09T14:40:27ZengIOP PublishingEnvironmental Research Letters1748-93262019-01-0114303400810.1088/1748-9326/aaf936Detecting global urban expansion over the last three decades using a fully convolutional networkChunyang He0Zhifeng Liu1https://orcid.org/0000-0002-4087-0743Siyuan Gou2Qiaofeng Zhang3Jinshui Zhang4Linlin Xu5Center 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, People’s Republic of China; School of Natural Resources, Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of ChinaCenter 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, People’s Republic of China; School of Natural Resources, Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of ChinaCenter 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, People’s Republic of China; School of Natural Resources, Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of ChinaDepartment of Geosciences, Murray State University , KY 42071, United States of AmericaESPRE, Beijing Normal University , Beijing 100875, People’s Republic of China; Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of China; Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of ChinaSchool of Land Science and Technology, China University of Geosciences , Beijing 100083, People’s Republic of ChinaThe 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 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.https://doi.org/10.1088/1748-9326/aaf936fully convolutional networkglobal urban expansiondeep learningnighttime light datavegetation indexland surface temperature |
| spellingShingle | Chunyang He Zhifeng Liu Siyuan Gou Qiaofeng Zhang Jinshui Zhang Linlin Xu Detecting global urban expansion over the last three decades using a fully convolutional network Environmental Research Letters fully convolutional network global urban expansion deep learning nighttime light data vegetation index land surface temperature |
| title | Detecting global urban expansion over the last three decades using a fully convolutional network |
| title_full | Detecting global urban expansion over the last three decades using a fully convolutional network |
| title_fullStr | Detecting global urban expansion over the last three decades using a fully convolutional network |
| title_full_unstemmed | Detecting global urban expansion over the last three decades using a fully convolutional network |
| title_short | Detecting global urban expansion over the last three decades using a fully convolutional network |
| title_sort | detecting global urban expansion over the last three decades using a fully convolutional network |
| topic | fully convolutional network global urban expansion deep learning nighttime light data vegetation index land surface temperature |
| url | https://doi.org/10.1088/1748-9326/aaf936 |
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