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<p>Urban open space (UOS) plays an important environmental role, especially in areas characterized by intense social and economic activity. However, the high interclass similarities, complex surroundings, and scale variations of UOS lead to unsatisfactory UOS mapping performance, and UOS mapping products for major cities around the world are lacking. To fill this gap, we used a deep learning-based method based on a tiny-manual annotation strategy and optical remote sensing imagery to produce a 1.19 m resolution UOS map of 169 megacities, namely the OpenspaceGlobal product. We generated the OpenspaceGlobal product with five urban open space categories. To obtain the final OpenspaceGlobal product, we processed over 8.5 TB of remote sensing images and nearly 90 million polygons in crowdsourced geospatial data. The validation results showed that the OpenspaceGlobal product had an overall accuracy of 79.13 % and a kappa coefficient of 73.47 %. The OpenspaceGlobal product can promote a better understanding of human-made space surfaces in major cities around the world.</p>

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The first urban open space product of global 169 megacities using remote sensing and geospatial data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月7日

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  • Runyu Fan,
  • Lizhe Wang,
  • Zijian Xu,
  • Hongyang Niu,
  • Jiajun Chen,
  • Zhaoying Zhou,
  • Wenyue Li,
  • Haoyu Wang,
  • Yuyue Sun,
  • Ruyi Feng

摘要

Urban open space (UOS) plays an important environmental role, especially in areas characterized by intense social and economic activity. However, the high interclass similarities, complex surroundings, and scale variations of UOS lead to unsatisfactory UOS mapping performance, and UOS mapping products for major cities around the world are lacking. To fill this gap, we used a deep learning-based method based on a tiny-manual annotation strategy and optical remote sensing imagery to produce a 1.19 m resolution UOS map of 169 megacities, namely the OpenspaceGlobal product. We generated the OpenspaceGlobal product with five urban open space categories. To obtain the final OpenspaceGlobal product, we processed over 8.5 TB of remote sensing images and nearly 90 million polygons in crowdsourced geospatial data. The validation results showed that the OpenspaceGlobal product had an overall accuracy of 79.13 % and a kappa coefficient of 73.47 %. The OpenspaceGlobal product can promote a better understanding of human-made space surfaces in major cities around the world.