Deep Semantic Segmentation for Rapid Extraction and Spatial-Temporal Expansion Variation Analysis of China’s Urban Built-Up Areas
Changes in the spatial expansion of urban built-up areas are of great significance for the analysis of China’s urbanization process and economic development. Nighttime light data can be used to extract urban built-up areas in a large-scale and long-time series. In this article, we introduced the UNe...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.883779/full |
| _version_ | 1811226996115505152 |
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| author | Maoyang Bai Shiqi Zhang Xiao Wang Yu Feng Juan Wang Peihao Peng Peihao Peng |
| author_facet | Maoyang Bai Shiqi Zhang Xiao Wang Yu Feng Juan Wang Peihao Peng Peihao Peng |
| author_sort | Maoyang Bai |
| collection | DOAJ |
| description | Changes in the spatial expansion of urban built-up areas are of great significance for the analysis of China’s urbanization process and economic development. Nighttime light data can be used to extract urban built-up areas in a large-scale and long-time series. In this article, we introduced the UNet model, a semantic segmentation network, as a base architecture, added spatial attention and channel attention modules to the encoder part to improve the boundary integrity and semantic consistency of the change feature map, and constructed an urban built-up area extraction model—CBAM_UNet. Also, we used this model to extract urban built-up areas from 2012 to 2021 and analyzed the spatial and temporal expansion of China’s urban built-up areas in terms of expansion speed, expansion intensity, expansion direction, and gravity center migration. In the last decade, the distribution pattern of urban built-up areas in China has gradually changed from “center” to “periphery-networked” distribution pattern. It reveals a trend from agglomeration to the dispersion of urban built-up areas in China. It provides a reference for China’s urban process and its economic development. |
| first_indexed | 2024-04-12T09:33:59Z |
| format | Article |
| id | doaj.art-d9048ba1e5bb4f7fa95f65f3eb556547 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-12T09:33:59Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-d9048ba1e5bb4f7fa95f65f3eb5565472022-12-22T03:38:17ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632022-07-011010.3389/feart.2022.883779883779Deep Semantic Segmentation for Rapid Extraction and Spatial-Temporal Expansion Variation Analysis of China’s Urban Built-Up AreasMaoyang Bai0Shiqi Zhang1Xiao Wang2Yu Feng3Juan Wang4Peihao Peng5Peihao Peng6College of Earth Sciences, Chengdu University of Technology, Chengdu, ChinaCollege of Earth Sciences, Chengdu University of Technology, Chengdu, ChinaCollege of Earth Sciences, Chengdu University of Technology, Chengdu, ChinaCollege of Earth Sciences, Chengdu University of Technology, Chengdu, ChinaCollege of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu, ChinaCollege of Earth Sciences, Chengdu University of Technology, Chengdu, ChinaCollege of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu, ChinaChanges in the spatial expansion of urban built-up areas are of great significance for the analysis of China’s urbanization process and economic development. Nighttime light data can be used to extract urban built-up areas in a large-scale and long-time series. In this article, we introduced the UNet model, a semantic segmentation network, as a base architecture, added spatial attention and channel attention modules to the encoder part to improve the boundary integrity and semantic consistency of the change feature map, and constructed an urban built-up area extraction model—CBAM_UNet. Also, we used this model to extract urban built-up areas from 2012 to 2021 and analyzed the spatial and temporal expansion of China’s urban built-up areas in terms of expansion speed, expansion intensity, expansion direction, and gravity center migration. In the last decade, the distribution pattern of urban built-up areas in China has gradually changed from “center” to “periphery-networked” distribution pattern. It reveals a trend from agglomeration to the dispersion of urban built-up areas in China. It provides a reference for China’s urban process and its economic development.https://www.frontiersin.org/articles/10.3389/feart.2022.883779/fullurban built-up areasdeep semantic segmentation networkCBAM_UNetspatial and temporal expansion of Chinacenter–periphery network |
| spellingShingle | Maoyang Bai Shiqi Zhang Xiao Wang Yu Feng Juan Wang Peihao Peng Peihao Peng Deep Semantic Segmentation for Rapid Extraction and Spatial-Temporal Expansion Variation Analysis of China’s Urban Built-Up Areas Frontiers in Earth Science urban built-up areas deep semantic segmentation network CBAM_UNet spatial and temporal expansion of China center–periphery network |
| title | Deep Semantic Segmentation for Rapid Extraction and Spatial-Temporal Expansion Variation Analysis of China’s Urban Built-Up Areas |
| title_full | Deep Semantic Segmentation for Rapid Extraction and Spatial-Temporal Expansion Variation Analysis of China’s Urban Built-Up Areas |
| title_fullStr | Deep Semantic Segmentation for Rapid Extraction and Spatial-Temporal Expansion Variation Analysis of China’s Urban Built-Up Areas |
| title_full_unstemmed | Deep Semantic Segmentation for Rapid Extraction and Spatial-Temporal Expansion Variation Analysis of China’s Urban Built-Up Areas |
| title_short | Deep Semantic Segmentation for Rapid Extraction and Spatial-Temporal Expansion Variation Analysis of China’s Urban Built-Up Areas |
| title_sort | deep semantic segmentation for rapid extraction and spatial temporal expansion variation analysis of china s urban built up areas |
| topic | urban built-up areas deep semantic segmentation network CBAM_UNet spatial and temporal expansion of China center–periphery network |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.883779/full |
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