Landscape Character Classification with a Deep Neural Network: A Case Study of the Jianghan Plain
Grounded in the theoretical and methodological frameworks of landscape character identification from the European Landscape Map (LANMAP) and landscape character assessment (LCA), this study developed an AI-based tool for landscape character analysis to classify the Jianghan Plain’s landscape more ef...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/13/12/2024 |
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| author | Wenke Qin Wenpeng Li Zhuohao Zhang Weiya Chen Min Wan |
| author_facet | Wenke Qin Wenpeng Li Zhuohao Zhang Weiya Chen Min Wan |
| author_sort | Wenke Qin |
| collection | DOAJ |
| description | Grounded in the theoretical and methodological frameworks of landscape character identification from the European Landscape Map (LANMAP) and landscape character assessment (LCA), this study developed an AI-based tool for landscape character analysis to classify the Jianghan Plain’s landscape more effectively. The proposed method leveraged a deep learning model, the artificial intelligence-based landscape character (AI-LC) classifier, along with specific naming and coding rules for the unique landscape character of the Jianghan Plain. Experimental results showed a significant improvement in classification accuracy, reaching 89% and 86% compared to traditional methods. The classifier identified 10 macro-level and 18 meso-level landscape character types within the region, which were further categorized into four primary zones—a lake network river basin, a hillfront terrace, surrounding mountains, and a lake network island hill—based on natural and social features. These advancements contributed to the theoretical framework of landscape character assessment, offering practical insights for landscape planning and conservation while highlighting AI’s transformative potential in environmental research and management. |
| format | Article |
| id | doaj-art-b668c7593c1e456393cab5d07e162c8c |
| institution | DOAJ |
| issn | 2073-445X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-b668c7593c1e456393cab5d07e162c8c2025-08-20T02:57:19ZengMDPI AGLand2073-445X2024-11-011312202410.3390/land13122024Landscape Character Classification with a Deep Neural Network: A Case Study of the Jianghan PlainWenke Qin0Wenpeng Li1Zhuohao Zhang2Weiya Chen3Min Wan4School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, ChinaGrounded in the theoretical and methodological frameworks of landscape character identification from the European Landscape Map (LANMAP) and landscape character assessment (LCA), this study developed an AI-based tool for landscape character analysis to classify the Jianghan Plain’s landscape more effectively. The proposed method leveraged a deep learning model, the artificial intelligence-based landscape character (AI-LC) classifier, along with specific naming and coding rules for the unique landscape character of the Jianghan Plain. Experimental results showed a significant improvement in classification accuracy, reaching 89% and 86% compared to traditional methods. The classifier identified 10 macro-level and 18 meso-level landscape character types within the region, which were further categorized into four primary zones—a lake network river basin, a hillfront terrace, surrounding mountains, and a lake network island hill—based on natural and social features. These advancements contributed to the theoretical framework of landscape character assessment, offering practical insights for landscape planning and conservation while highlighting AI’s transformative potential in environmental research and management.https://www.mdpi.com/2073-445X/13/12/2024landscape character assessmentEuropean landscape mapdeep learningJianghan Plain |
| spellingShingle | Wenke Qin Wenpeng Li Zhuohao Zhang Weiya Chen Min Wan Landscape Character Classification with a Deep Neural Network: A Case Study of the Jianghan Plain Land landscape character assessment European landscape map deep learning Jianghan Plain |
| title | Landscape Character Classification with a Deep Neural Network: A Case Study of the Jianghan Plain |
| title_full | Landscape Character Classification with a Deep Neural Network: A Case Study of the Jianghan Plain |
| title_fullStr | Landscape Character Classification with a Deep Neural Network: A Case Study of the Jianghan Plain |
| title_full_unstemmed | Landscape Character Classification with a Deep Neural Network: A Case Study of the Jianghan Plain |
| title_short | Landscape Character Classification with a Deep Neural Network: A Case Study of the Jianghan Plain |
| title_sort | landscape character classification with a deep neural network a case study of the jianghan plain |
| topic | landscape character assessment European landscape map deep learning Jianghan Plain |
| url | https://www.mdpi.com/2073-445X/13/12/2024 |
| work_keys_str_mv | AT wenkeqin landscapecharacterclassificationwithadeepneuralnetworkacasestudyofthejianghanplain AT wenpengli landscapecharacterclassificationwithadeepneuralnetworkacasestudyofthejianghanplain AT zhuohaozhang landscapecharacterclassificationwithadeepneuralnetworkacasestudyofthejianghanplain AT weiyachen landscapecharacterclassificationwithadeepneuralnetworkacasestudyofthejianghanplain AT minwan landscapecharacterclassificationwithadeepneuralnetworkacasestudyofthejianghanplain |