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Updated: Feb 8, 2026

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An R-Based Landscape Validation of a Competing Risk Model
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由人工智能驱动的景观价值映射绘制地图
David Jovanovikj1,2, Marija Stojcheva2, Viktor Domazetoski1,3,4
1Macedonian Academy of Sciences and Arts, Blvd. Krste Misirkov 2, 1000 Skopje, Republic of Macedonia.
Chaos (Woodbury, N.Y.)
|February 6, 2026
概括
人工智能-感知景观绘制 (AI-PLM) 使用社交媒体数据来理解人类的景观感知. 这个AI-PLM框架揭示了关键的欣赏热点和与罗马尼亚自然和遗产遗址的情感联系.
科学领域:
- 环境科学 环境科学
- 地理空间智能是什么?
- 计算社会科学 计算社会科学
背景情况:
- 传统的景观感知方法缺乏规模和范围.
- 了解人类的感知对于保护和规划至关重要.
- 社交媒体数据为景观研究提供了一个巨大的,尚未开发的资源.
研究的目的:
- 引入和验证人工智能感知景观绘图 (AI-PLM) 框架.
- 利用社交媒体数据建模人类对景观的集体感知.
- 通过人工智能驱动的分析来评估文化生态系统服务.
主要方法:
- 集成的AI-PLM框架结合了地理空间智能,机器学习和NLP.
- 来自罗马尼亚的地理标签的Flickr照片和用户评论的分析.
- 人工智能-空间认知 (Head/Tail Breaks,DBSCAN,视角分析) 和情感语义智能 (情感,话题建模).
主要成果:
- 确定了景观欣赏的强大的空间层次结构,在卡尔巴斯山脉,布拉索夫,布加勒斯特,马拉穆雷什和黑海沿岸的峰值.
- 情绪分析显示,与以自然为导向的地区相关的情绪主要是积极的.
- 主题建模突出了用户生成内容中的摄影,遗产和娱乐的主题.
结论:
- AI-PLM提供了一个可扩展和可转移的方法来评估文化生态系统服务.
- 该框架将物理地理和情感表达用于景观分析.
- AI-PLM为数据驱动的景观管理,保护和旅游规划提供了实用工具.
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