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

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Remote Sensing of Environment Pub Date : 2024-11-15 DOI:10.1016/j.rse.2024.114498
Yichen Lei , Xiuyuan Zhang , Shuping Xiong , Ge Tan , Shihong Du
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Abstract

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.
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开发分层遮挡感知模型:绘制中国 31 个城市的社区开放空间图
社区开放空间 (COS) 指的是社区内的细粒度和微型开放区域,它们为居民提供了方便的社交互动机会和健康益处。利用甚高分辨率(VHR)图像绘制社区开放空间图可以为监测城市可持续发展目标(SDGs)提供重要的社区尺度数据。然而,COS 的三维结构往往会导致二维卫星图像中的分层遮挡,从而导致 VHR 图像中地面 COS 特征的不可见性和支离破碎。本研究提出了一种新颖的分层遮挡感知模型(LOPM),通过对 COS 复杂的分层结构进行精确建模和重建来应对这些挑战。我们的方法包括自动生成一个全面的 COS 数据库,并联合训练一个深度学习网络来分解遮挡关系。所开发的双层地图产品 COS-1m 包括各种要素及其耦合空间,分辨率为 1 米,覆盖中国 31 个主要城市。结果表明,所提出的方法在这些城市的总体准确率达到了 86.39%,平均 F1 分数为 77.47%。COS-1m 显示,平均每个城市有 60.51 平方公里的 COS 区域被遮挡,占 COS 总面积的 10.18%。这项研究推动了 COS 分层监测技术的发展,通过提供精细的 COS 数据产品,填补了社区尺度可持续发展目标评估的关键空白,并为城市规划者和决策者提供了宝贵的见解,以促进更有效和可持续的城市发展。
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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