Evidence map›Paper›PMID 41028734›Full record

ArticleScientific data2025

CPVPD-2024: A Chinese photovoltaic plant dataset derived via a topography-enhanced deep learning framework.

Yang Yang, Shaofu Lin, Rongtian Lu, Xiliang Liu

Abstract read
In one paragraph

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yang YangCollege of Computer Science, Beijing University of Technology, Chaoyang District, Beijing, 100124, China.ORCID http://orcid.org/0009-0001-4314-4798
Shaofu LinCollege of Computer Science, Beijing University of Technology, Chaoyang District, Beijing, 100124, China. linshaofu@bjut.edu.cn.ORCID http://orcid.org/0000-0002-3653-6531
Rongtian LuCollege of Computer Science, Beijing University of Technology, Chaoyang District, Beijing, 100124, China.
Xiliang LiuCollege of Computer Science, Beijing University of Technology, Chaoyang District, Beijing, 100124, China. liuxl@bjut.edu.cn.ORCID http://orcid.org/0000-0001-5882-9389

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the largest global photovoltaic (PV) market, China experiences continuous rapid growth in PV installed capacity, playing a crucial role in achieving carbon peaking and neutrality goals through this central pillar of the energy transition. To address data fragmentation and inconsistency in current PV datasets, this study develops the 2024 China Photovoltaic Power Plant Vector Dataset (CPVPD-2024) using a deep semantic segmentation framework (DSFA-SwinNet) with geospatial verification. The dataset comprehensively covers all 34 provincial-level administrative regions of China, achieving an overall Precision of 90.38% and Intersection over Union (IoU) of 81.78% in test zones, demonstrating significant improvements in identifying PV array gaps and detecting small-scale distributed power plants. Research results indicate that the total installed PV area in China reached 4,520.47 km² by 2024, exhibiting a characteristic spatial pattern dominated by agrivoltaic systems with concentrated distribution in arid regions. As the first national panel-level PV vector dataset, it enables precise PV site selection, ecological assessments, and AI-driven remote sensing analysis.

Identifiers

PMID41028734
PMCPMC12485098

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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.