Scholarlink

<p>Multi-temporal mapping of the Green View Index (GVI) is crucial for understanding how urban residents perceive seasonal changes in streetscape greenness. Compared to street view imagery (SVI), remote sensing data offers higher temporal frequency and broader spatial coverage, enabling large-scale dynamic monitoring. However, most existing GVI estimation methods rely heavily on SVI, limiting their ability to support cross-city and seasonal analysis. To address this gap, we present the Seasonal Green View Index 2023 (SGVI-2023), a GVI mapping dataset derived from multisource remote sensing data and deep learning. Covering 19 major Chinese cities, SGVI-2023 was developed using approximately 1 million paired samples of satellite and SVI data collected from 2019 to 2023. All data underwent strict preprocessing and partitioning. Evaluation results show strong accuracy, with Pearson correlations of 0.867 at the point scale and 0.918 at the street scale. As the first cross-city, seasonally resolved GVI dataset based on remote sensing, SGVI-2023 provides valuable support for human-centered urban greenness monitoring and data-driven urban planning.</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

The First Seasonal Green View Index Mapping Dataset across Chinese cities powered by deep learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月5日

查看全文
  • Yibin Ma,
  • Pengfei Chen,
  • Mengjie Gong,
  • Yixian Cai,
  • Izzy Yi JIAN

摘要

Multi-temporal mapping of the Green View Index (GVI) is crucial for understanding how urban residents perceive seasonal changes in streetscape greenness. Compared to street view imagery (SVI), remote sensing data offers higher temporal frequency and broader spatial coverage, enabling large-scale dynamic monitoring. However, most existing GVI estimation methods rely heavily on SVI, limiting their ability to support cross-city and seasonal analysis. To address this gap, we present the Seasonal Green View Index 2023 (SGVI-2023), a GVI mapping dataset derived from multisource remote sensing data and deep learning. Covering 19 major Chinese cities, SGVI-2023 was developed using approximately 1 million paired samples of satellite and SVI data collected from 2019 to 2023. All data underwent strict preprocessing and partitioning. Evaluation results show strong accuracy, with Pearson correlations of 0.867 at the point scale and 0.918 at the street scale. As the first cross-city, seasonally resolved GVI dataset based on remote sensing, SGVI-2023 provides valuable support for human-centered urban greenness monitoring and data-driven urban planning.