3D-GloBFP:首个全球三维建筑足迹数据集

IF 11.2 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Hua Yuan, Yongjiu Dai
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引用次数: 0

摘要

摘要了解城市垂直结构,尤其是建筑高度,对于研究人类与其环境之间错综复杂的互动关系至关重要。此类数据集对于气候建模、能耗分析和社会经济活动等各种应用都是不可或缺的。尽管这些信息非常重要,但以往的研究主要集中在以网格为尺度估算区域内的建筑高度,因此数据集的覆盖范围或空间分辨率往往有限。这种局限性阻碍了全球综合分析以及在更细的尺度上产生可行见解的能力。在这项研究中,我们利用地球观测(EO)数据集和先进的机器学习技术,在建筑物足迹尺度上绘制了全球建筑物高度图(3D-GloBFP)。我们的方法整合了多源遥感特征和建筑形态特征,在全球不同地区使用极梯度提升(XGBoost)回归方法开发了高度估算模型。通过这种方法,我们估算出了全球各个建筑物的高度,并最终创建了首个全球三维(3-D)建筑物足迹(3D-GloBFP)。我们的评估结果表明,高度估算模型在全球范围内表现优异,33 个分区域的 R2 值从 0.66 到 0.96 不等,均方根误差 (RMSE) 从 1.9 米到 14.6 米不等。与其他数据集的比较表明,我们的 3D-GloBFP 与参考高度的分布和空间模式非常吻合。我们得出的三维全球建筑足迹图显示了不同地区、国家和城市之间建筑高度的明显空间模式,建筑高度从城市中心向周边农村地区逐渐降低。此外,我们的研究结果表明,不同国家和城市的已建基础设施(即建筑体量)存在差异。中国是已建基础设施总量最密集的国家(5.28×1011 立方米,占全球总量的 23.9%),其次是美国(3.90×1011 立方米,占全球总量的 17.6%)。在所有具有代表性的城市中,上海的基础设施建设量最大(2.1×1010 立方米)。得出的建筑足迹比例高度图(3D-GloBFP)揭示了城市建成环境的显著异质性,为城市社会经济动态和气候学研究提供了宝贵的见解。3D-GloBFP数据集可在以下网站获取:https://doi.org/10.5281/zenodo.11319913(3D-GloBFP中的美洲、非洲和大洋洲建筑高度)(Che等人,2024a)、https://doi.org/10.5281/zenodo.11397015(3D-GloBFP中的亚洲建筑高度)(Che等人,2024b)和https://doi.org/10.5281/zenodo.11391077(3D-GloBFP中的欧洲建筑高度)(Che等人,2024c)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
3D-GloBFP: the first global three-dimensional building footprint dataset
Abstract. Understanding urban vertical structures, particularly building heights, is essential for examining the intricate interaction between humans and their environment. Such datasets are indispensable for a variety of applications, including climate modeling, energy consumption analysis, and socioeconomic activities. Despite the importance of this information, previous studies have primarily focused on estimating building heights regionally on a grid scale, often resulting in datasets with limited coverage or spatial resolution. This limitation hampers comprehensive global analyses and the ability to generate actionable insights on finer scales. In this study, we developed a global building height map (3D-GloBFP) at a building footprint scale by leveraging Earth Observation (EO) datasets and advanced machine learning techniques. Our approach integrated multisource remote sensing features and building morphology features to develop height estimation models using the eXtreme Gradient Boosting (XGBoost) regression method across diverse global regions. This methodology allowed us to estimate the heights of individual buildings worldwide, culminating in the creation of the first global three-dimensional (3-D) building footprints (3D-GloBFP). Our evaluation results show that the height estimation models perform exceptionally well on a worldwide scale, with R2 ranging from 0.66 to 0.96 and root mean square errors (RMSEs) ranging from 1.9 m to 14.6 m across 33 subregions. Comparisons with other datasets demonstrate that our 3D-GloBFP closely matches the distribution and spatial pattern of reference heights. Our derived 3-D global building footprint map shows a distinct spatial pattern of building heights across regions, countries, and cities, with building heights gradually decreasing from the city center to the surrounding rural areas. Furthermore, our findings indicate the disparities in built-up infrastructure (i.e., building volume) across different countries and cities. China is the country with the most intensive total built-up infrastructure (5.28×1011 m3, accounting for 23.9 % of the global total), followed by the United States (3.90×1011 m3, accounting for 17.6 % of the global total). Shanghai has the largest volume of built-up infrastructure (2.1×1010 m3) of all representative cities. The derived building-footprint scale height map (3D-GloBFP) reveals the significant heterogeneity of urban built-up environments, providing valuable insights for studies in urban socioeconomic dynamics and climatology. The 3D-GloBFP dataset is available at https://doi.org/10.5281/zenodo.11319913 (Building height of the Americas, Africa, and Oceania in 3D-GloBFP) (Che et al., 2024a), https://doi.org/10.5281/zenodo.11397015 (Building height of Asia in 3D-GloBFP) (Che et al., 2024b), and https://doi.org/10.5281/zenodo.11391077 (Building height of Europe in 3D-GloBFP) (Che et al., 2024c).
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来源期刊
Earth System Science Data
Earth System Science Data GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
18.00
自引率
5.30%
发文量
231
审稿时长
35 weeks
期刊介绍: Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.
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