Urban heat islands and energy consumption patterns: Evaluating renewable energy strategies for a sustainable future DOI
Muhammad Khalid Anser, Abdelmohsen A. Nassani,

Khalid M. Al-Aiban

et al.

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 3760 - 3772

Published: March 27, 2025

Language: Английский

How does urban heat island differ across urban functional zones? Insights from 2D/3D urban morphology using geospatial big data DOI
Anqi Lin, Hao Wu, Wenting Luo

et al.

Urban Climate, Journal Year: 2023, Volume and Issue: 53, P. 101787 - 101787

Published: Dec. 14, 2023

Language: Английский

Citations

36
Quantifying the main and interactive effects of the dominant factors on the diurnal cycles of land surface temperature in typical urban functional zones DOI
Jike Chen, Kaixin Wang, Peijun Du

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 114, P. 105727 - 105727

Published: Aug. 13, 2024

Language: Английский

Citations

15
3D-GloBFP: the first global three-dimensional building footprint dataset DOI Creative Commons
Yangzi Che, Xuecao Li, Xiaoping Liu

et al.

Earth system science data, Journal Year: 2024, Volume and Issue: 16(11), P. 5357 - 5374

Published: Nov. 25, 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 a variety of applications, including climate modeling, energy consumption analysis, socioeconomic activities. Despite importance this information, previous studies have primarily focused on estimating heights regionally at grid scale, often resulting in with limited coverage or spatial resolution. This limitation hampers comprehensive global analysis ability to generate actionable insights finer scales. In study, we developed height map footprint scale by leveraging Earth Observation (EO) advanced machine learning techniques. Our approach integrated multisource remote-sensing features morphology develop estimation models using extreme gradient boosting (XGBoost) regression method across diverse regions. methodology allowed us estimate individual buildings worldwide, culminating creation three-dimensional (3D) Global Building Footprints (3D-GloBFP) dataset year 2020. evaluation results show that perform exceptionally well R2 values ranging from 0.66 0.96 root-mean-square errors (RMSEs) 1.9 14.6 m 33 subregions. Comparisons other demonstrate 3D-GloBFP closely matches distribution pattern reference heights. derived 3D shows distinct regions, countries, cities, gradually decreasing city center surrounding rural areas. Furthermore, our findings indicate disparities built-up infrastructure (i.e., volume) different countries cities. China country most intensive total (5.28×1011 m3, accounting 23.9 % total), followed USA (3.90×1011 17.6 total). Shanghai has largest volume (2.1×1010 m3) all representative The building-footprint-scale reveals significant heterogeneity environments, providing valuable dynamics climatology. available https://doi.org/10.5281/zenodo.11319912 (Building Americas, Africa, Oceania 3D-GloBFP; Che et al., 2024c), https://doi.org/10.5281/zenodo.11397014 Asia 2024a), https://doi.org/10.5281/zenodo.11391076 Europe 2024b).

Language: Английский

Citations

10
3D-GloBFP: the first global three-dimensional building footprint dataset DOI Creative Commons
Yangzi Che, Xuecao Li, Xiaoping Liu

et al.

Published: June 24, 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 a variety of applications, including climate modeling, energy consumption analysis, socioeconomic activities. Despite importance this information, previous studies have primarily focused on estimating heights regionally grid scale, often resulting in with limited coverage or spatial resolution. This limitation hampers comprehensive global analyses ability to generate actionable insights finer scales. In study, we developed height map (3D-GloBFP) at footprint scale by leveraging Earth Observation (EO) advanced machine learning techniques. Our approach integrated multisource remote sensing features morphology develop estimation models using eXtreme Gradient Boosting (XGBoost) regression method across diverse regions. methodology allowed us estimate individual buildings worldwide, culminating creation first three-dimensional (3-D) footprints (3D-GloBFP). evaluation results show that perform exceptionally well worldwide R2 ranging from 0.66 0.96 root mean square errors (RMSEs) 1.9 m 14.6 33 subregions. Comparisons other demonstrate our 3D-GloBFP closely matches distribution pattern reference heights. derived 3-D shows distinct regions, countries, cities, gradually decreasing city center surrounding rural areas. Furthermore, findings indicate disparities built-up infrastructure (i.e., volume) different countries cities. China country most intensive total (5.28×1011 m3, accounting 23.9 % total), followed United States (3.90×1011 17.6 total). Shanghai has largest volume (2.1×1010 m3) all representative The building-footprint reveals significant heterogeneity environments, providing valuable dynamics climatology. dataset available https://doi.org/10.5281/zenodo.11319913 (Building Americas, Africa, Oceania 3D-GloBFP) (Che et al., 2024a), https://doi.org/10.5281/zenodo.11397015 Asia 2024b), https://doi.org/10.5281/zenodo.11391077 Europe 2024c).

Language: Английский

Citations

9
Integrating morphology and vitality to quantify seasonal contributions of urban functional zones to thermal environment DOI
Lei Wang, Ruonan Li,

Jia Jia

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106136 - 106136

Published: Jan. 1, 2025

Language: Английский

Citations

1
Optimizing cooling efficiency of urban greenspaces across local climate zones in Wuhan, China DOI
Meng Cai, Miao Li, Huimin Liu

et al.

Urban forestry & urban greening, Journal Year: 2025, Volume and Issue: unknown, P. 128691 - 128691

Published: Jan. 1, 2025

Language: Английский

Citations

1
The nonlinear climatological impacts of urban morphology on extreme heats in urban functional zones: An interpretable ensemble learning-based approach DOI
Xiaochang Liu, Tao Wu, Qingrui Jiang

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: 273, P. 112728 - 112728

Published: Feb. 18, 2025

Language: Английский

Citations

1
Revealing the roles of climate, urban form, and vegetation greening in shaping the land surface temperature of urban agglomerations in the Yangtze River Economic Belt of China DOI

X. Liu,

Liang Zheng, Ying Wang

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 377, P. 124602 - 124602

Published: Feb. 20, 2025

Language: Английский

Citations

1
Multi-Scale Study of the Synergy Between Human Activities and Climate Change on Urban Heat Islands in China DOI
Kai-Hsiang Yang,

Jinting Zhang,

Dongge Cui

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106341 - 106341

Published: March 1, 2025

Language: Английский

Citations

1
Green space-building integration for Urban Heat Island mitigation: Insights from Beijing's fifth ring road district DOI
Zhifeng Wu, Yi Zhou, Yin Ren

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 105917 - 105917

Published: Oct. 1, 2024

Language: Английский

Citations

7