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Quality Evaluation of Public Spaces in Traditional Villages: A Study Using Deep Learning and Panoramic Images

Author

Listed:
  • Shiyu Meng

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Chenhui Liu

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Yuxi Zeng

    (Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Rongfang Xu

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Chaoyu Zhang

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Yuke Chen

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Kechen Wang

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Yunlu Zhang

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

Abstract

In the context of rapid urbanization, public spaces in traditional villages face challenges such as material ageing, loss of characteristics, and functional decline. The scientific and objective assessment of the quality of these public spaces is crucial for the sustainable development of traditional villages. Panoramic images, as an important source of spatial data, combined with deep learning technology, can objectively quantify the characteristics of public spaces in traditional villages. However, existing research has paid insufficient attention to the evaluation of the quality of public spaces in traditional villages at the micro-scale, often relying on questionnaires and interviews, which makes it difficult to meet the needs of planning and construction. This study constructs an evaluation system for the quality of public spaces in traditional villages, taking national-level traditional villages in the Fangshan District of Beijing as an example, based on traditional field research, using deep learning and panoramic images to automatically extract the features of public spaces in traditional villages, using a combination of the Analytic Hierarchy Process (AHP) and Criteria Importance Through Intercriteria Correlation (CRITIC) methods to determine the weights of the indicators and applying the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to evaluate the quality of public spaces in traditional villages. The study found that the quality of public spaces in Nanjiao Village is Grade I; Shuiyu Village and Liulinshui Village, Grade III; and Heilongguan Village, Grade IV and that there is still much room for improvement in general. The evaluation results match well with the public’s subjective perceptions, with an R 2 value of 0.832, proving that the constructed evaluation system has a high degree of accuracy. This study provides a scientific basis and an effective tool for the planning, design, and management of public spaces in traditional villages, which helps decision-makers better protect and utilize them.

Suggested Citation

  • Shiyu Meng & Chenhui Liu & Yuxi Zeng & Rongfang Xu & Chaoyu Zhang & Yuke Chen & Kechen Wang & Yunlu Zhang, 2024. "Quality Evaluation of Public Spaces in Traditional Villages: A Study Using Deep Learning and Panoramic Images," Land, MDPI, vol. 13(10), pages 1-24, September.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:10:p:1584-:d:1488582
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    References listed on IDEAS

    as
    1. Jie Zheng & Junjun He & Hongya Tang, 2022. "The Vitality of Public Space and the Effects of Environmental Factors in Chinese Suburban Rural Communities Based on Tourists and Residents," IJERPH, MDPI, vol. 20(1), pages 1-22, December.
    2. Lan Shen & Yikang Zhang & Minfeng Yao & Siren Lan, 2022. "Combination Weighting Integrated with TOPSIS for Landscape Performance Evaluation: A Case Study of Microlandscape from Rural Areas in Southeast China," Sustainability, MDPI, vol. 14(15), pages 1-26, August.
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