Mapping global annual urban land cover fractions (2001–2020) derived with multi-objective deep learning
Changes in urban land cover (ULC) provide critical evidence of urbanization including both urban expansion and internal structural renewal. Existing global urbanization research focused on urban expansion and neglected the dynamic ULC changes occurring inside urban areas. This study addresses this i...
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Elsevier
2025-02-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225000512 |
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| author | Haoyu Wang Qian Wang Xiuyuan Zhang Shihong Du Lubin Bai Shuping Xiong |
| author_facet | Haoyu Wang Qian Wang Xiuyuan Zhang Shihong Du Lubin Bai Shuping Xiong |
| author_sort | Haoyu Wang |
| collection | DOAJ |
| description | Changes in urban land cover (ULC) provide critical evidence of urbanization including both urban expansion and internal structural renewal. Existing global urbanization research focused on urban expansion and neglected the dynamic ULC changes occurring inside urban areas. This study addresses this issue by developing a Global Annual Urban Land Cover Fraction (GAULCF) dataset, which encompasses six ULC categories (Non-crop Vegetation-Cropland-Building-Non-build Impervious-Surface-Soil-Water, V-C-B-I-S-W) in global urban areas and measures their dynamics from 2001 to 2020. However, V-C-B-I-S-W six kinds of ULC extraction is much more difficult than classical V-I-S extraction, especially for distinguishing non-crop vegetation and cropland, buildings and non-build impervious. Accordingly, this study proposes a novel deep learning unmixing algorithm, Normalized Non-negative Multi-objective Residual T-ConvLSTM (NNMRT) model, with strong encoding and recognition capacities for extracting GAULCF. GAULCF has undergone rigorous accuracy assessment and third-party validation, whose standard error of regression, root mean square error, and mean absolute error are 0.127, 0.113, and 0.061, respectively. The GAULCF dataset reveals significant global ULC changes over the past 20 years: the impervious surface area nearly doubled, while building areas increased from 124,589 km2 to 206,603 km2; and vegetation and cropland were the predominant land cover types lost to urban expansion. GAULCF’s intensive monitoring of ULC changes during urbanization aids in identifying potential issues in urban development, providing crucial insights and supporting data for addressing global challenges and achieving sustainable urban development. |
| format | Article |
| id | doaj-art-7ec0f34070594e9c9e71687e63ae9685 |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-7ec0f34070594e9c9e71687e63ae96852025-02-09T04:59:51ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-01136104404Mapping global annual urban land cover fractions (2001–2020) derived with multi-objective deep learningHaoyu Wang0Qian Wang1Xiuyuan Zhang2Shihong Du3Lubin Bai4Shuping Xiong5College of Urban and Environmental Sciences, Peking University, Beijing, ChinaState Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100032, ChinaCollege of Urban and Environmental Sciences, Peking University, Beijing, China; Corresponding author.College of Urban and Environmental Sciences, Peking University, Beijing, ChinaInstitute of Remote Sensing and GIS, Peking University, Beijing 100871, ChinaInstitute of Remote Sensing and GIS, Peking University, Beijing 100871, ChinaChanges in urban land cover (ULC) provide critical evidence of urbanization including both urban expansion and internal structural renewal. Existing global urbanization research focused on urban expansion and neglected the dynamic ULC changes occurring inside urban areas. This study addresses this issue by developing a Global Annual Urban Land Cover Fraction (GAULCF) dataset, which encompasses six ULC categories (Non-crop Vegetation-Cropland-Building-Non-build Impervious-Surface-Soil-Water, V-C-B-I-S-W) in global urban areas and measures their dynamics from 2001 to 2020. However, V-C-B-I-S-W six kinds of ULC extraction is much more difficult than classical V-I-S extraction, especially for distinguishing non-crop vegetation and cropland, buildings and non-build impervious. Accordingly, this study proposes a novel deep learning unmixing algorithm, Normalized Non-negative Multi-objective Residual T-ConvLSTM (NNMRT) model, with strong encoding and recognition capacities for extracting GAULCF. GAULCF has undergone rigorous accuracy assessment and third-party validation, whose standard error of regression, root mean square error, and mean absolute error are 0.127, 0.113, and 0.061, respectively. The GAULCF dataset reveals significant global ULC changes over the past 20 years: the impervious surface area nearly doubled, while building areas increased from 124,589 km2 to 206,603 km2; and vegetation and cropland were the predominant land cover types lost to urban expansion. GAULCF’s intensive monitoring of ULC changes during urbanization aids in identifying potential issues in urban development, providing crucial insights and supporting data for addressing global challenges and achieving sustainable urban development.http://www.sciencedirect.com/science/article/pii/S1569843225000512UrbanizationUrban land coverUrban land cover fractionGAULCFV-C-B-I-S-WNNMRT |
| spellingShingle | Haoyu Wang Qian Wang Xiuyuan Zhang Shihong Du Lubin Bai Shuping Xiong Mapping global annual urban land cover fractions (2001–2020) derived with multi-objective deep learning International Journal of Applied Earth Observations and Geoinformation Urbanization Urban land cover Urban land cover fraction GAULCF V-C-B-I-S-W NNMRT |
| title | Mapping global annual urban land cover fractions (2001–2020) derived with multi-objective deep learning |
| title_full | Mapping global annual urban land cover fractions (2001–2020) derived with multi-objective deep learning |
| title_fullStr | Mapping global annual urban land cover fractions (2001–2020) derived with multi-objective deep learning |
| title_full_unstemmed | Mapping global annual urban land cover fractions (2001–2020) derived with multi-objective deep learning |
| title_short | Mapping global annual urban land cover fractions (2001–2020) derived with multi-objective deep learning |
| title_sort | mapping global annual urban land cover fractions 2001 2020 derived with multi objective deep learning |
| topic | Urbanization Urban land cover Urban land cover fraction GAULCF V-C-B-I-S-W NNMRT |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225000512 |
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