Developing Layered Occlusion Perception Model: Mapping community open spaces in 31 China cities

• Published in: Remote sensing of environment 2025-01, Vol.316, p.114498, Article 114498
• Main Authors: Lei, Yichen, Zhang, Xiuyuan, Xiong, Shuping, Tan, Ge, Du, Shihong
• Format: Article
• Language: English
• Subjects: China; Community open spaces; Deep learning; environment; Occlusion handling; remote sensing; social behavior; sustainable development; urban development
• ISSN: 0034-4257
• DOI: 10.1016/j.rse.2024.114498

Community Open Spaces (COS) refer to the fine-grained and micro-open areas within communities that offer residents convenient opportunities for social interaction and health benefits. The mapping of COS using Very High Resolution (VHR) imagery can provide critical community-scale data for monitoring urban sustainable development goals (SDGs). However, the three-dimensional structure of COS often results in layered occlusion in two-dimensional satellite imagery, leading to the invisibility and fragmentation of ground COS features in VHR images. This study presents a novel Layered Occlusion Perception Model (LOPM) to address these challenges by accurately modeling and reconstructing the intricate layered structure of COS. Our approach involves the automatic generation of a comprehensive COS database and the joint training of a deep learning network to decompose occlusion relationships. The developed dual-layer map product, COS-1m, includes various elements and their coupled spaces, with a resolution of 1 m, covering 31 major cities in China. The results demonstrate that the proposed method achieved an overall accuracy of 86.39% and an average F1-score of 77.47% across these cities. COS-1m reveals that, on average, 60.51 km2 of COS area per city is occluded, constituting 10.18% of the total COS area. This research advances the technology for layered monitoring of COS, fills a critical gap in community-scale SDG assessments by providing fine-grained COS data products, and offers valuable insights for urban planners and policymakers to promote more effective and sustainable urban development. •Hierarchical model for layer-occluded Community-level Open Spaces (COS) mapping.•Above-ground element knowledge guides the feature selection of ground elements.•Identify 10.18% of open spaces in China exhibit complex layer-coupled structures.•COS-1 m dataset produced covering 31 major cities in China with 1 m resolution.