A novel method for grading urban socio-economic development levels based on NTL data and Landsat data
The normalization of assessing urban socio-economic development levels contributed to the formulation of sound urban development strategies. Traditional methods predominantly dependent on socio-economic statistics, frequently resulted in exorbitant data collection costs, significant time lags, and i...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2527932 |
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| _version_ | 1849320195906076672 |
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| author | Xiang Hua Jiehai Cheng Yuke Meng Rongji Luo |
| author_facet | Xiang Hua Jiehai Cheng Yuke Meng Rongji Luo |
| author_sort | Xiang Hua |
| collection | DOAJ |
| description | The normalization of assessing urban socio-economic development levels contributed to the formulation of sound urban development strategies. Traditional methods predominantly dependent on socio-economic statistics, frequently resulted in exorbitant data collection costs, significant time lags, and incomplete spatial coverage. To address the deficiencies of current methods in terms of objectivity and real-time monitoring, this study integrated NTL and Landsat data with deep learning techniques to develop an automation identification methodology. This study developed a novel deep learning framework by integrating the VGG16 and U-Net architectures. Experimental results demonstrate that the proposed hybrid model achieves strong performance in predicting urban socio-economic development levels, with an accuracy of 86.2%. Analyses of the seven major urban agglomerations in the Yellow River Basin (2011–2022) revealed divergent trends: the Central Plains and Shandong Peninsula Urban Agglomerations maintained continuous development throughout the 12-year period, while the other five exhibited minimal changes in socio-economic development levels. |
| format | Article |
| id | doaj-art-bd9a89ce83814741a4cbd2bc3275ada7 |
| institution | Kabale University |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-bd9a89ce83814741a4cbd2bc3275ada72025-08-20T03:50:11ZengTaylor & Francis GroupGeocarto International1010-60491752-07622025-12-0140110.1080/10106049.2025.2527932A novel method for grading urban socio-economic development levels based on NTL data and Landsat dataXiang Hua0Jiehai Cheng1Yuke Meng2Rongji Luo3School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, ChinaThe normalization of assessing urban socio-economic development levels contributed to the formulation of sound urban development strategies. Traditional methods predominantly dependent on socio-economic statistics, frequently resulted in exorbitant data collection costs, significant time lags, and incomplete spatial coverage. To address the deficiencies of current methods in terms of objectivity and real-time monitoring, this study integrated NTL and Landsat data with deep learning techniques to develop an automation identification methodology. This study developed a novel deep learning framework by integrating the VGG16 and U-Net architectures. Experimental results demonstrate that the proposed hybrid model achieves strong performance in predicting urban socio-economic development levels, with an accuracy of 86.2%. Analyses of the seven major urban agglomerations in the Yellow River Basin (2011–2022) revealed divergent trends: the Central Plains and Shandong Peninsula Urban Agglomerations maintained continuous development throughout the 12-year period, while the other five exhibited minimal changes in socio-economic development levels.https://www.tandfonline.com/doi/10.1080/10106049.2025.2527932Urban socio-economic development levelsautomatic gradingYellow River Basinremote sensingdeep learning |
| spellingShingle | Xiang Hua Jiehai Cheng Yuke Meng Rongji Luo A novel method for grading urban socio-economic development levels based on NTL data and Landsat data Geocarto International Urban socio-economic development levels automatic grading Yellow River Basin remote sensing deep learning |
| title | A novel method for grading urban socio-economic development levels based on NTL data and Landsat data |
| title_full | A novel method for grading urban socio-economic development levels based on NTL data and Landsat data |
| title_fullStr | A novel method for grading urban socio-economic development levels based on NTL data and Landsat data |
| title_full_unstemmed | A novel method for grading urban socio-economic development levels based on NTL data and Landsat data |
| title_short | A novel method for grading urban socio-economic development levels based on NTL data and Landsat data |
| title_sort | novel method for grading urban socio economic development levels based on ntl data and landsat data |
| topic | Urban socio-economic development levels automatic grading Yellow River Basin remote sensing deep learning |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2025.2527932 |
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