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:
Towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping
Gabriel Kasmi, Laurent Dubus, Philippe Blanc, et al.
arXiv (Cornell University) (2022)
Open Access | Times Cited: 8
Gabriel Kasmi, Laurent Dubus, Philippe Blanc, et al.
arXiv (Cornell University) (2022)
Open Access | Times Cited: 8
Showing 8 citing articles:
Leveraging large-scale aerial data for accurate urban rooftop solar potential estimation via multitask learning
A. Boccalatte, Ankit Jha, Jocelyn Chanussot
Solar Energy (2025) Vol. 290, pp. 113336-113336
Closed Access | Times Cited: 1
A. Boccalatte, Ankit Jha, Jocelyn Chanussot
Solar Energy (2025) Vol. 290, pp. 113336-113336
Closed Access | Times Cited: 1
A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata
Gabriel Kasmi, Yves‐Marie Saint‐Drenan, David Trebosc, et al.
Scientific Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 34
Gabriel Kasmi, Yves‐Marie Saint‐Drenan, David Trebosc, et al.
Scientific Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 34
Deriving the orientation of existing solar energy systems from LiDAR data at scale
David Lingfors, Robert Johansson, Johan Lindahl
Solar Energy (2025) Vol. 291, pp. 113344-113344
Open Access
David Lingfors, Robert Johansson, Johan Lindahl
Solar Energy (2025) Vol. 291, pp. 113344-113344
Open Access
A Comparative Evaluation of Deep Learning Techniques for Photovoltaic Panel Detection From Aerial Images
Edoardo Arnaudo, Giacomo Blanco, Antonino Monti, et al.
IEEE Access (2023) Vol. 11, pp. 47579-47594
Open Access | Times Cited: 12
Edoardo Arnaudo, Giacomo Blanco, Antonino Monti, et al.
IEEE Access (2023) Vol. 11, pp. 47579-47594
Open Access | Times Cited: 12
Identifying small decentralized solar systems in aerial images using deep learning
Âzeddine Frimane, Robert Johansson, Joakim Munkhammar, et al.
Solar Energy (2023) Vol. 262, pp. 111822-111822
Open Access | Times Cited: 7
Âzeddine Frimane, Robert Johansson, Joakim Munkhammar, et al.
Solar Energy (2023) Vol. 262, pp. 111822-111822
Open Access | Times Cited: 7
Remote Sensing-Based Estimation of Rooftop Photovoltaic Power Production Using Physical Conversion Models and Weather Data
Gabriel Kasmi, Augustin Touron, Philippe Blanc, et al.
(2024)
Open Access | Times Cited: 1
Gabriel Kasmi, Augustin Touron, Philippe Blanc, et al.
(2024)
Open Access | Times Cited: 1
Toward global rooftop PV detection with Deep Active Learning
Matthias Zech, Hendrik-Pieter Tetens, Joseph Ranalli
Advances in Applied Energy (2024), pp. 100191-100191
Open Access | Times Cited: 1
Matthias Zech, Hendrik-Pieter Tetens, Joseph Ranalli
Advances in Applied Energy (2024), pp. 100191-100191
Open Access | Times Cited: 1
PyPVRoof: a Python package for extracting the characteristics of rooftop PV installations using remote sensing data
Yann Trémenbert, Gabriel Kasmi, Laurent Dubus, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 1
Yann Trémenbert, Gabriel Kasmi, Laurent Dubus, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 1
