GABLE: A first fine-grained 3D building model of China on a national scale from very high resolution satellite imagery

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 305, P. 114057 - 114057

Published: Feb. 27, 2024

Three-dimensional (3D) building models provide horizontal and vertical information of urban development patterns, which are significant to urbanization analysis, solar energy planning, carbon reduction sustainability. Despite that many popular products on a global or national scale proposed, these usually focus extraction height estimation at fairly coarse resolutions while categories not taken into consideration. In this study, we extend the previous work in two aspects involving introduction semantically fine-grained (i.e., 12 rooftop classes) spatially representations individual buildings with compact polygons. Specifically, develop novel framework for generation 3D models, including developing network joint classification, another parallel estimation, post-processing algorithm fusion results from independent networks. To train networks improve generalization, construct custom large-scale datasets addition existing Urban Building Classification (UBC) dataset 2023 IEEE Data Fusion Contest (DFC 2023) dataset. Finally, nation-scale fine-GrAined BuiLding modEl (GABLE) product is derived based Beijing-3 satellite images (0.5–0.8 m) our proposed framework. GABLE provides polygon, category value each instance. Further analyses conducted uncover distribution terms diversity, density. These demonstrate significance values GALBE, potentials far beyond these.

Language: Английский

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

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

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Mapping material stocks in buildings and infrastructures across the Beijing–Tianjin–Hebei urban agglomeration at high-resolution using multi-source geographical data

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

Published: March 22, 2024

Language: Английский

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Impact of urban space on PM2.5 distribution: A multiscale and seasonal study in the Yangtze River Delta urban agglomeration

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 363, P. 121287 - 121287

Published: June 5, 2024

Language: Английский

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

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

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Structure-aware deep learning network for building height estimation

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: unknown, P. 104443 - 104443

Published: Feb. 1, 2025

Language: Английский

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A building volume adjusted nighttime light index for characterizing the relationship between urban population and nighttime light intensity

Computers Environment and Urban Systems, Journal Year: 2022, Volume and Issue: 99, P. 101911 - 101911

Published: Oct. 27, 2022

Language: Английский

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Deep learning-based building height mapping using Sentinel-1 and Sentinel-2 data

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2023, Volume and Issue: 122, P. 103399 - 103399

Published: June 30, 2023

Accurately mapping building height at a fine scale is crucial for comprehending urban systems. However, existing methods suffer from limitations such as coarse resolutions, long delays, and limited applicability large-scale mapping. This challenge particularly significant in China, where rapid urbanization has led to complex scenario. To address this issue, we propose novel approach that capitalizes on publicly available Sentinel-1/-2 crowdsourced data. Our method employs dual-branch structure estimation network (BHE-NET) an improved multi-modal Selective-Kernel (MSK) module fuse optical SAR features. The validation results, derived data across 63 cities, demonstrate strong performance with root mean square error (RMSE) of 4.65 m. We further test the scalability our by three most developed agglomerations China. In comparison four recent studies, captures finer details while mitigating overestimation high-density clusters. Moreover, investigate relationship between population well volume city level. work opens up new possibilities producing fine-scale map China 10-m resolution using remote sensing

Language: Английский

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Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building Footprints

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(2), P. 263 - 263

Published: Jan. 9, 2024

Accurately estimating building heights is crucial for various applications, including urban planning, climate studies, population estimation, and environmental assessment. However, this remains a challenging task, particularly large areas. Satellite-based Light Detection Ranging (LiDAR) has shown promise, but it often faces difficulties in distinguishing photons from other ground objects. To address challenge, we propose novel method that incorporates footprints, relative positions of photons, self-adaptive buffer photon selection. We employ the Ice, Cloud, Land Elevation Satellite 2 (ICESat-2) photon-counting LiDAR, specifically ICESat-2/ATL03 data, along with footprints obtained New York City (NYC) Open Data platform. The proposed approach was applied to estimate 17,399 buildings NYC, results showed strong consistency reference heights. root mean square error (RMSE) 8.1 m, 71% buildings, absolute (MAE) less than 3 m. Furthermore, conducted an extensive evaluation thoroughly investigated influence terrain, region, height, density, parameter also verified effectiveness our experimental area Beijing compared existing methods. By leveraging ICESat-2 LiDAR advanced selection techniques, demonstrates potential accurately over broad

Language: Английский

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Characterizing the 3-D structure of each building in the conterminous United States

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 105, P. 105318 - 105318

Published: Feb. 27, 2024

Language: Английский

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