A gradient-based nonlinear multi-pixel physical method for simultaneously separating component temperature and emissivity from nonisothermal mixed pixels with DART DOI Creative Commons
Zhijun Zhen, Shengbo Chen, Nicolas Lauret

и другие.

Remote Sensing of Environment, Год журнала: 2025, Номер 324, С. 114738 - 114738

Опубликована: Апрель 17, 2025

Язык: Английский

Hy-TeC: a hybrid vision transformer model for high-resolution and large-scale mapping of canopy height DOI Creative Commons
Ibrahim Fayad, Philippe Ciais, Martin A. Schwartz

и другие.

Remote Sensing of Environment, Год журнала: 2023, Номер 302, С. 113945 - 113945

Опубликована: Дек. 27, 2023

Язык: Английский

Процитировано

34

Remote sensing of diverse urban environments: From the single city to multiple cities DOI Creative Commons
Gang Chen, Yuyu Zhou, James A. Voogt

и другие.

Remote Sensing of Environment, Год журнала: 2024, Номер 305, С. 114108 - 114108

Опубликована: Март 14, 2024

Язык: Английский

Процитировано

17

A deep learning-based super-resolution method for building height estimation at 2.5 m spatial resolution in the Northern Hemisphere DOI Creative Commons
Yinxia Cao, Qihao Weng

Remote Sensing of Environment, Год журнала: 2024, Номер 310, С. 114241 - 114241

Опубликована: Июнь 4, 2024

Язык: Английский

Процитировано

12

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

и другие.

Earth system science data, Год журнала: 2024, Номер 16(11), С. 5357 - 5374

Опубликована: Ноя. 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).

Язык: Английский

Процитировано

12

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

и другие.

Resources Conservation and Recycling, Год журнала: 2024, Номер 205, С. 107561 - 107561

Опубликована: Март 22, 2024

Язык: Английский

Процитировано

11

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

и другие.

Опубликована: Июнь 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).

Язык: Английский

Процитировано

10

Global spatial patterns between nighttime light intensity and urban building morphology DOI Creative Commons
Bin Wu, Hailan Huang, Yu Wang

и другие.

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2023, Номер 124, С. 103495 - 103495

Опубликована: Сен. 20, 2023

The comprehensive characterization of global urbanization requires consideration both human activities and urban physical structures. Both structures exhibit regular self-similar patterns, yet the spatial patterns between two at a scale remain elusive. This study utilized NPP-VIIRS annual composite data newly available world settlement footprint 3D to investigate relationships nighttime light intensity building morphological indicators across several scales. Our results demonstrated that there is weak association morphology pixel level, as shown by maximum correlation coefficient approximately 0.4, but strong provincial/state level with over 0.8. Additionally, we performed an urban-rural gradient analysis evaluate indicators. indicated dominant gradients for morphologies follow declining trend from centers rural areas. Notably, inconsistencies were found predominantly in Africa. findings also suggested can be served indicator urbanization, thus provide implications facilitating solutions aimed reducing income disparity promoting sustainable development.

Язык: Английский

Процитировано

19

Understanding spontaneous biodiversity in informal urban green spaces: A local-landscape filtering framework with a test on wall plants DOI

Xinyu Miao,

Yuhan Pan, Hanxu Chen

и другие.

Urban forestry & urban greening, Год журнала: 2023, Номер 86, С. 127996 - 127996

Опубликована: Июнь 8, 2023

Язык: Английский

Процитировано

14

China's first sub-meter building footprints derived by deep learning DOI
Xin Huang, Zhen Zhang, Jiayi Li

и другие.

Remote Sensing of Environment, Год журнала: 2024, Номер 311, С. 114274 - 114274

Опубликована: Июнь 18, 2024

Язык: Английский

Процитировано

6

Utilizing Building Offset and Shadow to Retrieve Urban Building Heights with ICESat-2 Photons DOI Creative Commons
Bin Wu, Hailan Huang, Yi Zhao

и другие.

Remote Sensing, Год журнала: 2023, Номер 15(15), С. 3786 - 3786

Опубликована: Июль 30, 2023

Building height serves as an essential feature of urban morphology that provides valuable insights into human socio-cultural behaviors and their impact on the environment in milieu. However, openly accessible building information at individual level is still lacking remains sorely limited. Previous studies have shown ICESat-2′s ATL03/08 products are good accuracy for heights retrieval, however, these limited to areas with available data coverage. To this end, we propose a method extracting by using ICESat-2 ATL03 photons high-resolution remote sensing images. We first extracted roof footprint offsets shadows from high resolution imagery multitasking CNN frameworks. Using samples calculated photons, developed estimation combines offset shadow length information. assessed efficacy proposed Wujiaochang area Shanghai city, China. The results indicated able extract MAE 4.7 m, outperforms traditional shadow-based offset-based method. believe candidate accurately retrieving city-wide scale.

Язык: Английский

Процитировано

12