Investigating the Structural Health of High-Rise Buildings and Its Influencing Factors Using Sentinel-1 Synthetic Aperture Radar Imagery: A Case Study of the Guangzhou–Foshan Metropolitan Area DOI Creative Commons
Di Huang, Zhixin Qi,

Suya Lin

и другие.

Buildings, Год журнала: 2024, Номер 14(12), С. 4074 - 4074

Опубликована: Дек. 21, 2024

Urban growth is increasingly shifting from horizontal expansion to vertical development, resulting in skylines dominated by high-rise buildings. The post-construction operations and maintenance of these buildings are critical, requiring regular structural health monitoring (SHM) proactively identify address potential safety concerns. Interferometric synthetic aperture radar (InSAR) has proven effective for building safety, but most studies rely on high-resolution (SAR) images. high cost limited coverage images restrict their use large-scale monitoring. Sentinel-1 medium-resolution SAR images, which freely available offer broad coverage, make SHM more feasible. However, the monitoring, especially at large spatial scales, remain limited. To this gap, study, PS-InSAR technology proposed performing a comprehensive assessment super Guangzhou–Foshan Metropolitan Area (GFMA) analyzing influencing factors. Our shows that while overall satisfactory, certain areas, including Pearl River New Town, central Huadu district Guangzhou, southeastern Shunde Foshan, exhibit suboptimal conditions. We verified findings using GNSS data on-site investigations, confirming imagery offers reliable accuracy health. Furthermore, we identified factors such as settlement soft soil layers, construction surrounding (underground) infrastructure, aging, could potentially impact safety. results demonstrate provide reliable, rapid, cost-effective method stability, enhancing our understanding underlying mechanisms informing strategies prevent crises, also ensuring sustainable development society.

Язык: Английский

The Impact of Urban Spatial Forms on Marine Cooling Effects in Mainland and Island Regions: A Case Study of Xiamen, China DOI

Yuanping Shen,

Qiaqia Zhang,

Qunyue Liu

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106210 - 106210

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

3

Refined Urban Functional Zones Identification via Empirical Bayesian Kriging: A POI-Weighted Scoring Innovation DOI Creative Commons

Du Xuan,

Y.B. Pan, Xiaoyan Yang

и другие.

IEEE Access, Год журнала: 2025, Номер 13, С. 22784 - 22799

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1

Structure-aware deep learning network for building height estimation DOI Creative Commons
Yuehong Chen, Jiayue Zhou, Congcong Xu

и другие.

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер unknown, С. 104443 - 104443

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1

Spatio-temporal evolution of vertical urban growth in China’s Yangtze River Delta from 1990 to 2020 DOI
Chenglong Yin, Ruishan Chen,

Xiangming Xiao

и другие.

Land Use Policy, Год журнала: 2025, Номер 153, С. 107542 - 107542

Опубликована: Март 23, 2025

Язык: Английский

Процитировано

0

Inequities in Thermal Comfort and Urban Blue-Green Spaces Cooling: An Explainable Machine Learning Study Across Residents of Different Socioeconomic Statuses in Hangzhou, China DOI
Yufei Liu, Guie Li

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106427 - 106427

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0

Mapping global annual urban land cover fractions (2001–2020) derived with multi-objective deep learning DOI Creative Commons
Haoyu Wang, Wang Qian, Xiuyuan Zhang

и другие.

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 136, С. 104404 - 104404

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

Community-Level Urban Vitality Intensity and Diversity Analysis Supported by Multisource Remote Sensing Data DOI Creative Commons
Zhiran Zhang,

Jiping Liu,

Yangyang Zhao

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(6), С. 1056 - 1056

Опубликована: Март 17, 2025

Urban vitality serves as a crucial metric for evaluating sustainable urban development and the well-being of residents. Existing studies have predominantly focused on analyzing direct effects intensity (VI) its influencing factors, while paying less attention to diversity (VD) indirect impact mechanisms. Supported by multisource remote sensing data, this study establishes five-dimensional evaluation system employs Partial Least Squares Structural Equation Model (PLS-SEM) quantify interrelationships between these multidimensional factors VI/VD. The findings are follows: (1) Spatial divergence VI VD: exhibited stronger clustering (I = 1.12), aggregating in central areas, whereas VD demonstrated moderate autocorrelation 0.45) concentrated mixed-use or suburban zones. (2) Drivers intensity: strongly associated with commercial density (β 0.344) transportation accessibility 0.253), but negatively correlated natural environment quality (r −0.166). (3) Mechanisms diversity: is closely linked public service 0.228). This research provides valuable insights city decision-making, particularly strengthening optimizing functional layouts.

Язык: Английский

Процитировано

0

Urban growth unveiled: Deep learning with satellite imagery for measuring 3D building-stock evolution in Urban China DOI
Sebastiano Papini, Susie Xi Rao,

Sapar Charyyev

и другие.

Remote Sensing Applications Society and Environment, Год журнала: 2025, Номер unknown, С. 101523 - 101523

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

A segmented approach to modeling building height: Delineating high-rise and low-rise buildings for enhanced height estimation DOI

Clinton Stipek,

Daniel Adams, Philipe Dias

и другие.

Computers Environment and Urban Systems, Год журнала: 2025, Номер 119, С. 102287 - 102287

Опубликована: Апрель 8, 2025

Язык: Английский

Процитировано

0

Characterizing dynamics of built-up height in China from 2005 to 2020 based on GEDI, Landsat, and PALSAR data DOI
Peimin Chen, Huabing Huang, Peng Qin

и другие.

Remote Sensing of Environment, Год журнала: 2025, Номер 325, С. 114776 - 114776

Опубликована: Апрель 26, 2025

Язык: Английский

Процитировано

0