U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling DOI Creative Commons
Yifan Cheng, Lei Zhao, TC Chakraborty

et al.

Earth system science data, Journal Year: 2025, Volume and Issue: 17(5), P. 2147 - 2174

Published: May 21, 2025

Abstract. High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset represent spatial heterogeneity surfaces their biophysical properties. This deficiency long obstructed development urban-resolving Earth system models (ESMs) ultra-high-resolution modeling, over large domains. Here, we present U-Surf, first-of-its-kind 1 km resolution present-day (circa 2020) global continuous surface parameter dataset. Using canopy model (UCM) in Community System Model as base for satisfying requirements, U-Surf leverages latest advances remote sensing, machine learning, cloud computing provide most relevant parameters, including radiative, morphological, thermal properties, UCMs at facet level. Generated using systematically unified workflow, ensures internal consistency among key making it first coherent significantly improves representation land both within across cities globally; provides essential, high-fidelity constraints ESMs; enables detailed city-to-city comparisons globe; supports next-generation kilometer-resolution scales. parameters can be easily converted or adapted various types UCMs, such those embedded weather regional models, well air quality models. The fundamental provided by also used features learning have other broad-scale applications socioeconomic, public health, planning contexts. We expect advance research frontier science, climate-sensitive design, coupled human–Earth systems future. is publicly available https://doi.org/10.5281/zenodo.11247598 (Cheng et al., 2024).

Language: Английский

Drivers of global surface urban heat islands: Surface property, climate background, and 2D/3D urban morphologies DOI

Ledi Shao,

Weilin Liao, Peilin Li

et al.

Building and Environment, Journal Year: 2023, Volume and Issue: 242, P. 110581 - 110581

Published: July 4, 2023

Language: Английский

Citations

55
Global Mapping of Three-Dimensional (3D) Urban Structures Reveals Escalating Utilization in the Vertical Dimension and Pronounced Building Space Inequality DOI Creative Commons

Xiaoping Liu,

Xinxin Wu, Xuecao Li

et al.

Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: March 1, 2024

Three-dimensional (3D) urban structures play a critical role in informing climate mitigation strategies aimed at the built environment and facilitating sustainable development. Regrettably, there exists significant gap detailed consistent data on 3D building space with global coverage due to challenges inherent collection model calibration processes. In this study, we constructed structure dataset (GUS-3D), including volume, height, footprint information, 500 m spatial resolution using extensive satellite observation products numerous reference samples. Our analysis indicated that total volume of buildings worldwide 2015 exceeded 1 × 1012 m3. Over 1985 period, observed slight increase magnitude growth (i.e., it increased from 166.02 km3 during 1985–2000 period 175.08 2000–2015 period), while expansion magnitudes two-dimensional (2D) (22.51 103 km2 vs. 13.29 km2) extent (157 133.8 notably decreased. This trend highlights intensive vertical utilization land. Furthermore, identified heterogeneity provision inequality across cities worldwide. is particularly pronounced many populous Asian cities, which has been overlooked previous studies economic inequality. The GUS-3D shows great potential deepen our understanding creates new horizons for studies.

Language: Английский

Citations

19
Global 30 meters spatiotemporal 3D urban expansion dataset from 1990 to 2010 DOI Creative Commons
Tingting He, Kechao Wang, Wu Xiao

et al.

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: May 26, 2023

Abstract Understanding the spatiotemporal dynamics of global 3D urban expansion over time is becoming increasingly crucial for achieving long-term development goals. In this study, we generated a dataset annual (1990–2010) using World Settlement Footprint 2015 data, GAIA and ALOS AW3D30 data with three-step technical framework: (1) extracting constructed land to generate research area, (2) neighborhood analysis calculate original normalized DSM slope height each pixel in study (3) correction areas greater than 10° improve accuracy estimated building heights. The cross-validation results indicate that our reliable United States(R 2 = 0.821), Europe(R 0.863), China(R 0.796), across world(R 0.811). As know, first 30-meter globe, which can give unique information understand address implications urbanization on food security, biodiversity, climate change, public well-being health.

Language: Английский

Citations

33
Combining ICESat-2 photons and Google Earth Satellite images for building height extraction DOI Creative Commons
Yi Zhao, Bin Wu, Qiaoxuan Li

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2023, Volume and Issue: 117, P. 103213 - 103213

Published: Jan. 29, 2023

Building heights are one of the crucial data for comprehending functions urban systems. Employing optical remote sensing imagery, shadow-based method is most promising methods which have been proposed estimating building height. However, existing studies height estimation restricted to a small area due lack annotations and ignorance azimuth variations. The Ice, Cloud, Land Elevation Satellite-2 (ICESat-2) allows large-scale retrieval in along-track direction thus can be taken as ground truth support algorithms extraction. Here, we an approach extracting by combining Google Earth Satellite (GES) images ICESat-2 photons. shadow instances were first extracted using U-Net deep learning framework. Based on retrieved from photons, improved model minimizing global error across all sample buildings was developed. A typical located city center Shanghai, China with around 90 km2 selected validate method. In total 15,966 successfully results indicated that estimated high accuracy absolute mean 4.08 m. Moreover, shows better performance compared datasets. holds great potential building-level contributes further morphologies.

Language: Английский

Citations

31
Advances on the global human settlement layer by joint assessment of earth observation and population survey data DOI Creative Commons
Martino Pesaresi, Marcello Schiavina, Panagiotis Politis

et al.

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: Aug. 30, 2024

The Global Human Settlement Layer (GHSL) project fosters an enhanced, public understanding of the human presence on Earth. A decade after its inception in Digital Earth 2020 vision, GHSL is established European Commission's Joint Research Centre and integral part Copernicus Emergency Management Service. 2023 edition, a result rigorous research Observation data population censuses, contributes significantly to worldwide settlements. It introduces new elements like 10-m-resolution, sub-pixel estimation built-up surfaces, global building height volume estimates, classification residential non-residential areas, improving density grids. This paper evaluates GHSL's key components, including Symbolic Machine Learning approach, using novel reference data. These enable comparative assessment model predictions evolution surface, heights, resident population. Empirical evidence suggests that estimates are most accurate domain today (e.g. IoU 0.98 water class, 0.92 0.8 6% MAE for 100 m surface or 2.27 height, 83% TAA population). consolidates theoretical foundation highlights innovative features transparent Artificial Intelligence, facilitating international decision-making processes.

Language: Английский

Citations

16
Spatiotemporal patterns and drivers of the urban air pollution island effect for 2273 cities in China DOI Creative Commons
Lu Niu,

Zhang Zhi,

Yingzi Liang

et al.

Environment International, Journal Year: 2024, Volume and Issue: 184, P. 108455 - 108455

Published: Jan. 21, 2024

Air pollution levels tend to be higher in urban areas than surrounding rural areas, and this air has a negative effect on human health. However, the spatiotemporal patterns of urban-rural differences determinants these remain unclear. Here, we calculate Urban Pollution Island (UAPI) intensity for PM2.5 PM10 monthly, seasonal, annual scale 2273 cities China from 2000 2020. Subsequently, analyze influence characteristics using combined approach two-way fixed effects model spatial Durbin model. Results show strong downward trend UAPI since 2013, with reductions ranging 42% 61% until 2020, both pollutants summer as well winter,.. Consistently, proportion experiencing phenomenon decreased 94.5% 77.3% PM10. We find significant morphology UAPI. Specifically, sprawl, polycentric development, an increase green spaces are associated reduction UAPI, while dense intensify it. Our study also reveals robust inverted U-shaped relationship between stages economic development Moreover, itself spillover that oppose their direct impacts. These results suggest regional planning more ambitious climate change mitigation policies could effective strategies mitigating end-of-pipe control.

Language: Английский

Citations

12
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

11
Mapping material stocks in buildings and infrastructures across the Beijing–Tianjin–Hebei urban agglomeration at high-resolution using multi-source geographical data DOI
Bowen Cai, André Baumgart, Helmut Haberl

et al.

Resources Conservation and Recycling, Journal Year: 2024, Volume and Issue: 205, P. 107561 - 107561

Published: March 22, 2024

Language: Английский

Citations

9
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
A global urban tree leaf area index dataset for urban climate modeling DOI Creative Commons

Wenzong Dong,

Hua Yuan, Wanyi Lin

et al.

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 12, 2025

Abstract Urban trees are recognized for mitigating urban thermal stress, therefore incorporating their effects is crucial climate research. However, due to the limitation of remote sensing, LAI in areas generally masked (e.g., MODIS), which turn limits its application Canopy Models (UCMs). To address this gap, we developed a high-resolution (500 m) and long-time-series (2000–2022) tree dataset derived through Random Forest model trained with MODIS data, help meteorological variables height datasets. The results show that our has high accuracy when validated against site reference maps, R 0.85 RMSE 1.03 m 2 /m . Compared reprocessed LAI, modeled exhibits an ranging from 0.36 0.64 0.89 0.97 globally. This provides reasonable representation terms magnitude seasonal changes, thereby potentially enhancing applications UCMs studies.

Language: Английский

Citations

1