Enhancing Urban Land Utilization Through SegFormer: A Vacant Land Analysis in Chengdu
Urban vacant land (UVL) presents significant environmental and urban planning challenges as cities expand, necessitating effective identification and management strategies. This study proposes an enhanced framework for UVL extraction, based on an improved SegFormer model, which incorporates the dens...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10873816/ |
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| author | Xi Cheng Jieyu Yang Bin Li Bin Zhao Deng Pan Zhanfeng Shen Qian Zhu Miaomiao Liu |
| author_facet | Xi Cheng Jieyu Yang Bin Li Bin Zhao Deng Pan Zhanfeng Shen Qian Zhu Miaomiao Liu |
| author_sort | Xi Cheng |
| collection | DOAJ |
| description | Urban vacant land (UVL) presents significant environmental and urban planning challenges as cities expand, necessitating effective identification and management strategies. This study proposes an enhanced framework for UVL extraction, based on an improved SegFormer model, which incorporates the densely connected atrous spatial pyramid pooling module and the progressive feature pyramid network for expanded receptive field and achieve multiscale feature integration. The framework first applies a region-based stratification approach, dividing the study area into the central and expanded areas to handle varying land characteristics in different urban regions. Both pretrained and non-pretrained models were utilized to assess their effectiveness in segmentation accuracy, using high-resolution remote sensing images of Chengdu. The experimental results demonstrate the effectiveness of the framework, with the pretrained model, trained on urbanized area data from Chinese cities, achieving <italic>F</italic>1-scores of 91.34 and 90.05 and IoU values of 84.21 and 81.91 for the central and expanded areas, respectively. In contrast, the non-pretrained model yielded <italic>F</italic>1-scores of 93.08 and 92.32, with corresponding IoU values of 87.16 and 85.74. Ablation studies and robustness tests further confirm the model's stability and precision in complex application scenarios. This framework provides the accurate and efficient tool for UVL identification, contributing to improved urban land utilization and offering valuable insights for future research and urban planning. |
| format | Article |
| id | doaj-art-d29bd4a8346d4328a4f2bf57c00c25f5 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-d29bd4a8346d4328a4f2bf57c00c25f52025-08-20T02:03:34ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01186070608510.1109/JSTARS.2025.353892010873816Enhancing Urban Land Utilization Through SegFormer: A Vacant Land Analysis in ChengduXi Cheng0https://orcid.org/0000-0002-0587-8651Jieyu Yang1https://orcid.org/0009-0001-1969-2559Bin Li2Bin Zhao3Deng Pan4Zhanfeng Shen5https://orcid.org/0000-0002-8651-7435Qian Zhu6Miaomiao Liu7https://orcid.org/0009-0004-2814-5372College of Geophysics, Chengdu University of Technology, Chengdu, ChinaCollege of Geophysics, Chengdu University of Technology, Chengdu, ChinaCollege of Geophysics, Chengdu University of Technology, Chengdu, ChinaCollege of Geophysics, Chengdu University of Technology, Chengdu, ChinaCollege of Geophysics, Chengdu University of Technology, Chengdu, ChinaNational Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaCollege of Geophysics, Chengdu University of Technology, Chengdu, ChinaCollege of Geophysics, Chengdu University of Technology, Chengdu, ChinaUrban vacant land (UVL) presents significant environmental and urban planning challenges as cities expand, necessitating effective identification and management strategies. This study proposes an enhanced framework for UVL extraction, based on an improved SegFormer model, which incorporates the densely connected atrous spatial pyramid pooling module and the progressive feature pyramid network for expanded receptive field and achieve multiscale feature integration. The framework first applies a region-based stratification approach, dividing the study area into the central and expanded areas to handle varying land characteristics in different urban regions. Both pretrained and non-pretrained models were utilized to assess their effectiveness in segmentation accuracy, using high-resolution remote sensing images of Chengdu. The experimental results demonstrate the effectiveness of the framework, with the pretrained model, trained on urbanized area data from Chinese cities, achieving <italic>F</italic>1-scores of 91.34 and 90.05 and IoU values of 84.21 and 81.91 for the central and expanded areas, respectively. In contrast, the non-pretrained model yielded <italic>F</italic>1-scores of 93.08 and 92.32, with corresponding IoU values of 87.16 and 85.74. Ablation studies and robustness tests further confirm the model's stability and precision in complex application scenarios. This framework provides the accurate and efficient tool for UVL identification, contributing to improved urban land utilization and offering valuable insights for future research and urban planning.https://ieeexplore.ieee.org/document/10873816/Chengduhigh-resolution remote sensingSegFormer modelsemantic segmentationvacant land identification |
| spellingShingle | Xi Cheng Jieyu Yang Bin Li Bin Zhao Deng Pan Zhanfeng Shen Qian Zhu Miaomiao Liu Enhancing Urban Land Utilization Through SegFormer: A Vacant Land Analysis in Chengdu IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Chengdu high-resolution remote sensing SegFormer model semantic segmentation vacant land identification |
| title | Enhancing Urban Land Utilization Through SegFormer: A Vacant Land Analysis in Chengdu |
| title_full | Enhancing Urban Land Utilization Through SegFormer: A Vacant Land Analysis in Chengdu |
| title_fullStr | Enhancing Urban Land Utilization Through SegFormer: A Vacant Land Analysis in Chengdu |
| title_full_unstemmed | Enhancing Urban Land Utilization Through SegFormer: A Vacant Land Analysis in Chengdu |
| title_short | Enhancing Urban Land Utilization Through SegFormer: A Vacant Land Analysis in Chengdu |
| title_sort | enhancing urban land utilization through segformer a vacant land analysis in chengdu |
| topic | Chengdu high-resolution remote sensing SegFormer model semantic segmentation vacant land identification |
| url | https://ieeexplore.ieee.org/document/10873816/ |
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