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Landscape Character Classification with a Deep Neural Network: A Case Study of the Jianghan Plain

Author

Listed:
  • Wenke Qin

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Wenpeng Li

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zhuohao Zhang

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Weiya Chen

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    National Center of Technology Innovation for Digital Construction, Huazhong University of Science & Technology, Wuhan 430074, China
    International Joint Research Laboratory of Smart Construction, Wuhan 430074, China)

  • Min Wan

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

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.

Suggested Citation

  • Wenke Qin & Wenpeng Li & Zhuohao Zhang & Weiya Chen & Min Wan, 2024. "Landscape Character Classification with a Deep Neural Network: A Case Study of the Jianghan Plain," Land, MDPI, vol. 13(12), pages 1-20, November.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:12:p:2024-:d:1531091
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    References listed on IDEAS

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    1. Haozun Sun & Hong Xu & Hao He & Quanfeng Wei & Yuelin Yan & Zheng Chen & Xuanhe Li & Jialun Zheng & Tianyue Li, 2023. "A Spatial Analysis of Urban Streets under Deep Learning Based on Street View Imagery: Quantifying Perceptual and Elemental Perceptual Relationships," Sustainability, MDPI, vol. 15(20), pages 1-30, October.
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    3. Yang, Ran & Li, Xiaoyan & Mao, Dehua & Wang, Zongming & Cheng, Lisha & Dong, Yulin & Sun, Hongchao, 2024. "A methodological framework for prioritizing wetland restoration from cropland: A case study Jianghan Plain, China," Land Use Policy, Elsevier, vol. 137(C).
    4. Yingxue Wang & Jiaheng Du & Jingxing Kuang & Chunxu Chen & Maobiao Li & Jin Wang, 2023. "Two-Scaled Identification of Landscape Character Types and Areas: A Case Study of the Yunnan–Vietnam Railway (Yunnan Section), China," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
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