COS-1m: the first community-scale open space maps of 31 major cities in China created with the layered occlusion perception model

Correspondence to:Shihong DuPeking UniversityBeijing 100871, ChinaE-mail: [e-mail]

COS-1m (Community Open Spaces) offers detailed, high-resolution maps of community-level open spaces across 31 major cities in China. Developed using the latest very high-resolution satellite imagery and a novel Layered Occlusion Perception Model (LOPM), this dataset ensures precise representation with a spatial resolution of 1 meter. In total, the COS-1m dataset covers an area of 46727.46 square kilometers.

The COS-1m dataset addresses the critical challenge of layered occlusions in remote sensing imagery by accurately modeling and reconstructing the intricate layered structures of COS. This approach allows for the detailed delineation of occluded ground elements, overcoming the limitations of traditional single-layer mapping methods. The dataset reveals that, on average, 60.51 km² of COS area per city is occluded, constituting 10.18% of the total COS area, which had previously been overlooked.

Key features of the COS-1m dataset include:

Four Types of Public Open Space Elements: Green spaces (1), blue spaces (2), squares (4), and walkways (6).

Three Types of Coupled Open Spaces: waterfront woodlands (3), shaded squares (5), and shaded walkways (7).

The dual-layer mapping technique used in COS-1m provides a comprehensive overview of urban open spaces, capturing the vertical overlaps of various elements such as trees, water bodies, squares, and walkways. This dataset is a significant advancement in COS monitoring technology, filling a critical gap in community-scale Sustainable Development Goal (SDG) assessments. It provides urban planners and policymakers with valuable insights to promote more effective and sustainable urban development.

COS-1m achieved an overall accuracy of 86.39% and an average F1-score of 77.47% across the 31 cities, demonstrating its robustness and reliability as a tool for urban space analysis and planning.

If you use this dataset, please cite: "Lei, Y., Zhang, X., Xiong, S., Tan, G., & Du, S. (2025). Developing Layered Occlusion Perception Model: Mapping community open spaces in 31 China cities. Remote Sensing of Environment, 316, 114498."