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3D-GloBFP: the first global three-dimensional building footprint dataset.

Authors :
Che, Yangzi
Li, Xuecao
Liu, Xiaoping
Wang, Yuhao
Liao, Weilin
Zheng, Xianwei
Zhang, Xucai
Xu, Xiaocong
Shi, Qian
Zhu, Jiajun
Yuan, Hua
Dai, Yongjiu
Source :
Earth System Science Data Discussions; 6/24/2024, p1-28, 28p
Publication Year :
2024

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 R <superscript>2</superscript> 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 (5.28×10<superscript>11</superscript> m<superscript>3</superscript>, accounting for 23.9 % of the global total), followed by the United States (3.90×10<superscript>11</superscript> m<superscript>3</superscript>, accounting for 17.6 % of the global total). Shanghai has the largest volume of built-up infrastructure (2.1×10<superscript>10</superscript> m<superscript>3</superscript>) 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). [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18663591
Database :
Complementary Index
Journal :
Earth System Science Data Discussions
Publication Type :
Academic Journal
Accession number :
178044810
Full Text :
https://doi.org/10.5194/essd-2024-217