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Geographic Information Visualization and Sustainable Development of Low-Carbon Rural Slow Tourism under Artificial Intelligence

Author

Listed:
  • Gongyi Jiang

    (Foreign Languages Department, Tourism College of Zhejiang, Hangzhou 310043, China
    Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan)

  • Weijun Gao

    (Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan
    Innovation Institute for Sustainable Maritime Architecture Research and Technology, Qingdao University of Technology, Qingdao 266033, China)

  • Meng Xu

    (Chemical Engineering and Technology, Zhejiang University of Technology, Hangzhou 310014, China)

  • Mingjia Tong

    (Foreign Languages Department, Tourism College of Zhejiang, Hangzhou 310043, China)

  • Zhonghui Liu

    (School of Municipal and Environmental Engineering, Jilin Jianzhu University, Changchun 130118, China)

Abstract

This study conducts in-depth research on geographic information visualization and the sustainable development of low-carbon rural slow tourism under artificial intelligence (AI) to analyze and discuss the visualization of geographic information and the sustainable development of low-carbon slow tourism in rural areas. First, the development options related to low-carbon tourism in rural areas are discussed. Then, a low-carbon rural slow tourism recommendation method based on AI and a low-carbon rural tourism scene recognition method based on Cross-Media Retrieval (CMR) data are proposed. Finally, the proposed scheme is tested. The test results show that the carbon dioxide emissions of one-day tourism projects account for less than 10% of the total tourism industry. From the proportion, it is found that air transport accounts for the largest proportion, more than 40%. With the development of time, the number of rural slow tourists in Guizhou has increased the most, while the number of rural slow tourists in Yunnan has increased to a lesser extent. In the K-means clustering model, the accuracy of scenario classification based on the semantic features of scene attributes is 5.26% higher than that of attribute likelihood vectors. On the Support Vector Machine classifier, the scene classification accuracy based on the semantic features of scene attributes is 19.2% higher than that of the scene classification based on attribute likelihood vector features. CMR techniques have also played a satisfying role in identifying rural tourism scenarios. They enable passengers to quickly identify tourist attractions to save preparation time and provide more flexible time for the tour process. The research results have made certain contributions to the sustainable development of low-carbon rural slow tourism.

Suggested Citation

  • Gongyi Jiang & Weijun Gao & Meng Xu & Mingjia Tong & Zhonghui Liu, 2023. "Geographic Information Visualization and Sustainable Development of Low-Carbon Rural Slow Tourism under Artificial Intelligence," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3846-:d:1074478
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    References listed on IDEAS

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