Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A deep learning-based super-resolution method for building height estimation at 2.5 m spatial resolution in the Northern Hemisphere
Yinxia Cao, Qihao Weng
Remote Sensing of Environment (2024) Vol. 310, pp. 114241-114241
Open Access | Times Cited: 11

Showing 11 citing articles:

Structure-aware deep learning network for building height estimation
Yuehong Chen, Jiayue Zhou, Congcong Xu, et al.
International Journal of Applied Earth Observation and Geoinformation (2025), pp. 104443-104443
Open Access
CMAB: A Multi-Attribute Building Dataset of China
Yecheng Zhang, Huimin Zhao, Ying Long
Scientific Data (2025) Vol. 12, Iss. 1
Open Access
High-quality one-shot interactive segmentation for remote sensing images via hybrid adapter-enhanced foundation models
Zhili Zhang, Xiangyun Hu, Yue Yang, et al.
International Journal of Applied Earth Observation and Geoinformation (2025) Vol. 139, pp. 104466-104466
Closed Access
Spatio-temporal evolution of vertical urban growth in China’s Yangtze River Delta from 1990 to 2020
Chenglong Yin, Ruishan Chen, Xiangming Xiao, et al.
Land Use Policy (2025) Vol. 153, pp. 107542-107542
Closed Access
Application of Deep Learning on Global Spaceborne Radar and Multispectral Imagery for the Estimation of Urban Surface Height Distribution
Vivaldi Rinaldi, Masoud Ghandehari
Remote Sensing (2025) Vol. 17, Iss. 7, pp. 1297-1297
Open Access
A segmented approach to modeling building height: Delineating high-rise and low-rise buildings for enhanced height estimation
Clinton Stipek, Daniel Adams, Philipe Dias, et al.
Computers Environment and Urban Systems (2025) Vol. 119, pp. 102287-102287
Closed Access
Characterizing dynamics of built-up height in China from 2005 to 2020 based on GEDI, Landsat, and PALSAR data
Peimin Chen, Huabing Huang, Peng Qin, et al.
Remote Sensing of Environment (2025) Vol. 325, pp. 114776-114776
Closed Access
Combining visual intelligence and social-physical urban features facilitates fine-scale seasonality characterization of urban thermal environments
Jiahua Yu, Qiao Hu, Jiating Li
Building and Environment (2024), pp. 112088-112088
Closed Access | Times Cited: 2
How high are we? Large-scale building height estimation at 10 m using Sentinel-1 SAR and Sentinel-2 MSI time series
Ritu Yadav, Andrea Nascetti, Yifang Ban
Remote Sensing of Environment (2024) Vol. 318, pp. 114556-114556
Open Access | Times Cited: 2
High-resolution population mapping based on SDGSAT-1 glimmer imagery and deep learning: a case study of the Guangdong-Hong Kong-Marco Greater Bay Area
Haoxuan Duan, Zhongqi Shi, Ji Ge, et al.
International Journal of Digital Earth (2024) Vol. 17, Iss. 1
Open Access
Derivation of Surface Models Using Satellite Imagery Deep Learning Architectures with Explainable AI
Rinaldi Vivaldi, Francisco Gómez-Vela, Masoud Ghandehari
Results in Engineering (2024), pp. 103436-103436
Open Access
SIGHT: Stacked Integration of Geospatial Hierarchical Typologies for Inferring Building Characteristics
Daniel Adams, Jessica Moehl, Clinton Stipek, et al.
2021 IEEE International Conference on Big Data (Big Data) (2024), pp. 5775-5784
Closed Access
Page 1

Scroll to top