Department of Hydraulic Engineering, Tsinghua Univeristy, Beijing, China.
Institute for Risk and Disaster Reduction, University College London, London, UK.
Sci Data. 2024 Jun 12;11(1):618. doi: 10.1038/s41597-024-03446-2.
Understanding building morphology is crucial for accurately simulating interactions between urban structures and hydroclimate dynamics. Despite significant efforts to generate detailed global building morphology datasets, there is a lack of practical solutions using publicly accessible resources. In this work, we present GLAMOUR, a dataset derived from open-source Sentinel imagery that captures the average building height and footprint at a resolution of 0.0009 across urbanized areas worldwide. Validated in 18 cities, GLAMOUR exhibits superior accuracy with median root mean square errors of 7.5 m and 0.14 for building height and footprint estimations, indicating better overall performance against existing published datasets. The GLAMOUR dataset provides essential morphological information of 3D building structures and can be integrated with other datasets and tools for a wide range of applications including 3D building model generation and urban morphometric parameter derivation. These extended applications enable refined hydroclimate simulation and hazard assessment on a broader scale and offer valuable insights for researchers and policymakers in building sustainable and resilient urban environments prepared for future climate adaptation.
理解建筑物形态对于准确模拟城市结构与水气候动态之间的相互作用至关重要。尽管已经做出了巨大努力来生成详细的全球建筑物形态数据集,但在利用公开可用资源方面,仍然缺乏切实可行的解决方案。在这项工作中,我们提出了 GLAMOUR,这是一个源自开源 Sentinel 图像的数据集,它以全球城市化区域 0.0009 的分辨率捕捉平均建筑物高度和占地面积。在 18 个城市进行验证后,GLAMOUR 表现出卓越的准确性,其建筑物高度和占地面积的中位数均方根误差分别为 7.5 米和 0.14,这表明其整体性能优于现有的已发表数据集。GLAMOUR 数据集提供了 3D 建筑物结构的基本形态信息,可与其他数据集和工具集成,用于广泛的应用,包括 3D 建筑物模型生成和城市形态参数推导。这些扩展应用可实现更精细的水气候模拟和更广泛的灾害评估,并为研究人员和决策者提供有价值的见解,以建设为未来气候适应做好准备的可持续和有弹性的城市环境。