Scholarlink

<p>Great Wall individual heritage buildings (IHBs) are of historical importance but increasingly threatened by vegetation-induced erosion. Effective conservation requires monitoring vegetation dynamics at the site-scale, yet precise spatial data for IHBs is lacking. To address this, we propose a comprehensive framework for site-scale vegetation shift analysis based on automatic IHB detection. First, we develop IHBSegNet, a deep learning segmentation network with three core modules to accurately extract IHB footprints. We then apply an unsupervised clustering algorithm to classify vegetation density from 10 m satellite embedding data. Finally, per-pixel class shifts are quantified and aggregated within each IHB for vegetation shift analysis. Case studies along the Great Wall sections in Fugu, Shenmu, and Yuyang show that IHBSegNet outperforms state-of-the-art networks, achieving an IoU of 73.02%. Analysis of detected IHBs revealed significant vegetation density shifts at 67 sites, with densification observed in Fugu (26 sites) and Yuyang (25 sites).</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Automatic detection and site-scale vegetation shift analysis for individual heritage buildings along the Great Wall

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月17日

查看全文
  • Daoyuan Zheng,
  • Shaohua Wang,
  • Haixia Feng,
  • Tiancheng Liu,
  • Yizhou Lan,
  • Shunli Wang,
  • Xujie Zhang,
  • Mingyao Ai,
  • Pengcheng Zhao,
  • Jiayuan Li,
  • Qingwu Hu

摘要

Great Wall individual heritage buildings (IHBs) are of historical importance but increasingly threatened by vegetation-induced erosion. Effective conservation requires monitoring vegetation dynamics at the site-scale, yet precise spatial data for IHBs is lacking. To address this, we propose a comprehensive framework for site-scale vegetation shift analysis based on automatic IHB detection. First, we develop IHBSegNet, a deep learning segmentation network with three core modules to accurately extract IHB footprints. We then apply an unsupervised clustering algorithm to classify vegetation density from 10 m satellite embedding data. Finally, per-pixel class shifts are quantified and aggregated within each IHB for vegetation shift analysis. Case studies along the Great Wall sections in Fugu, Shenmu, and Yuyang show that IHBSegNet outperforms state-of-the-art networks, achieving an IoU of 73.02%. Analysis of detected IHBs revealed significant vegetation density shifts at 67 sites, with densification observed in Fugu (26 sites) and Yuyang (25 sites).