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

Wenke Qin, Wenpeng Li, Zhuohao Zhang, Weiya Chen () and Min Wan ()
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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
Min Wan: School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China

Land, 2024, vol. 13, issue 12, 1-20

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.

Keywords: landscape character assessment; European landscape map; deep learning; Jianghan Plain (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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