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<p>The scaled utilization of cultivated land has enhanced agricultural development and productivity. Quantifying its spatial distribution is essential for optimizing agricultural decision-making. Xinjiang, a vital grain production region in China, holds paramount study significance due to its distinct geographical location and fragile natural environment. However, most studies on cultivated land fragmentation rely on outdated raster datasets. In this study, we introduce a cultivated land dataset of Xinjiang in a vector form with higher boundary accuracy, and more suitable for cultivated land statistics. A novel parcel extraction method that integrates the Swin Transformer for multi-scale semantic information and DiffusionEdge for capturing fine boundary details is proposed, which enhances the accuracy of land parcel extraction from 10-meter resolution Sentinel-2 imagery, obtained from the Copernicus Open Access Hub. Finally, we present a practical and up-to-date vector dataset of cultivated land. The Technical Validation analysis substantiates the reliability and applicability of the dataset. Through this study, we contribute to developing a replicable methodology for robust cultivated land extraction and parcel-wise cultivated land analysis.</p>

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Parcel-level vector data for scaled land utilization analysis in Xinjiang based on remote sensing image

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月16日

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  • Wei Wu,
  • Yikai Zhao,
  • Liao Yang,
  • You Zeng,
  • Rui Liu,
  • Shuangyan Huang,
  • Weisheng Wang,
  • Xiande Wu

摘要

The scaled utilization of cultivated land has enhanced agricultural development and productivity. Quantifying its spatial distribution is essential for optimizing agricultural decision-making. Xinjiang, a vital grain production region in China, holds paramount study significance due to its distinct geographical location and fragile natural environment. However, most studies on cultivated land fragmentation rely on outdated raster datasets. In this study, we introduce a cultivated land dataset of Xinjiang in a vector form with higher boundary accuracy, and more suitable for cultivated land statistics. A novel parcel extraction method that integrates the Swin Transformer for multi-scale semantic information and DiffusionEdge for capturing fine boundary details is proposed, which enhances the accuracy of land parcel extraction from 10-meter resolution Sentinel-2 imagery, obtained from the Copernicus Open Access Hub. Finally, we present a practical and up-to-date vector dataset of cultivated land. The Technical Validation analysis substantiates the reliability and applicability of the dataset. Through this study, we contribute to developing a replicable methodology for robust cultivated land extraction and parcel-wise cultivated land analysis.