Scholarlink

<p>Accurate land cover mapping is essential for supporting ecological protection and resource management. However, in regions with complex and fragmented landscapes, such as Southern China (SC), existing medium- and high-resolution land cover products exhibit limited accuracy. To address this challenge, we developed Superpixel_U-Net, a multi-head segmentation algorithm that combines semantic segmentation and superpixel segmentation, to generate a 1.5 m land cover map for SC (the SCLC map). Towards this purpose, we acquired 6.1 TB very-high-resolution (VHR) ESRI World imagery and manually annotated 43,000 VHR images into eight land cover classes. Vegetation and water indices derived from Sentinel-2 imagery were used as auxiliary inputs to enrich spectral features. The resulting SCLC map achieved Pixel Accuracy (PA) values of 88.83% and 90.66% in the pixel-level and patch-level validations respectively, and showed a strong agreement with official survey reports (R<sup>2</sup> = 0.88). This dataset provides a solid foundation for further analyses that require accurate delineation of land cover types in SC.</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

A 1.5 m resolution land cover map of Southern China created with Superpixel U-Net

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月9日

查看全文
  • Xiaomei Hu,
  • Wenkai Li,
  • Zhenong Jin,
  • Asadilla Yusup,
  • Shihao Zhu,
  • Ziyan Yang,
  • Shengli Tao

摘要

Accurate land cover mapping is essential for supporting ecological protection and resource management. However, in regions with complex and fragmented landscapes, such as Southern China (SC), existing medium- and high-resolution land cover products exhibit limited accuracy. To address this challenge, we developed Superpixel_U-Net, a multi-head segmentation algorithm that combines semantic segmentation and superpixel segmentation, to generate a 1.5 m land cover map for SC (the SCLC map). Towards this purpose, we acquired 6.1 TB very-high-resolution (VHR) ESRI World imagery and manually annotated 43,000 VHR images into eight land cover classes. Vegetation and water indices derived from Sentinel-2 imagery were used as auxiliary inputs to enrich spectral features. The resulting SCLC map achieved Pixel Accuracy (PA) values of 88.83% and 90.66% in the pixel-level and patch-level validations respectively, and showed a strong agreement with official survey reports (R2 = 0.88). This dataset provides a solid foundation for further analyses that require accurate delineation of land cover types in SC.