ArticleScientific data2025
Global photovoltaic solar panel dataset from 2019 to 2022.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- High-resolution mapping reveals a profound gap between existing rooftop solar deployment and potential in Africa.iScience · 2026Article
- Reply to Yang et al.: Synergies between photovoltaic development and innovative agricultural practices for combating desertification.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Labeled photovoltaic installations for orthographic aerial imagery in Queens, New York.Scientific data · 2026Article
- CPVPD-2024: A Chinese photovoltaic plant dataset derived via a topography-enhanced deep learning framework.Scientific data · 2025Article
- Global photovoltaic solar panel dataset from 2019 to 2022.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Solar photovoltaic (PV) power generation, known for its affordability and environmental benefits, is a key component of the global energy supply. However, the lack of comprehensive, timely, and precise global PV datasets has limited spatial analysis of PV potential. We developed a new method to identify PV panels globally, producing an annual 20-meter resolution dataset for 2019-2022. This dataset offers unprecedented detail and accuracy for future research and policy-making. A two-stage PV classification framework was built using U-Net and positive unlabelled learning with random forest (PUL-RF). U-Net first recognizes PVs from sub-meter Google Earth images, expanding positive PV samples for the second stage, where PUL-RF classifies Sentinel-2 images on a large scale. The dataset was evaluated with IoU and F1-Score metrics, achieving over 90% accuracy. Compared to existing datasets, it provides better precision and spatial detail, showing global PV growth of over 60% between 2019 and 2022, with developing countries leading the increase.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.