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  • TEAM 研究チーム Research Team
  • MOVIE 動画 Movie
  • CASE 事例紹介 Case Studies
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TOP  Research Team   Improving the Efficiency of Forest Resource Assessment Using a Forest Vegetation Classification AI with Rapid Training Image Generation
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    Improving the Efficiency of Forest Resource Assessment Using a Forest Vegetation Classification AI with Rapid Training Image Generation

    代表機関名 Kyoto University
    採択年度 2024
    フェーズ Phase 0
    URL https://fserc.kyoto-u.ac.jp/wp/staff/ise

    メール takeshi.ise.lab[@]gmail.com

    By extending and improving the “Fragmented Image Method,” which enables rapid creation of training images, we will build forest vegetation classification AI models with less than one‑tenth of the labor time required by conventional AI approaches. This significant cost reduction will bring innovation to forestry sites where AI adoption has previously been limited. By enabling more efficient forest management, the technology will help improve profitability while also contributing to the mitigation of labor shortages in the forestry sector.

    メール takeshi.ise.lab[@]gmail.com

    URL https://fserc.kyoto-u.ac.jp/wp/staff/ise
    研究代表者

    伊勢 武史

    京都大学

    Improving the Efficiency of Forest Resource Assessment Using a Forest Vegetation Classification AI with Rapid Training Image Generation

Group 1032
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