ArticleScientific data2024
A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Comparing potential biodiversity conflicts from renewable energy expansion in China at different centralization levels.Nature ecology & evolution · 2026Article
- Spatial distribution and environmental attributes dataset of China's large-scale data centers in 2024.Scientific data · 2026Article
- Dynamic maps of plastic-mulched farmlands in Northeast China from 1985 to 2025.Scientific data · 2026Article
- Reveal the deployable solar energy potential and emission reduction benefits in the arid areas of Xinjiang.Scientific reports · 2026Article
- A multi-label dataset for China's agricultural and rural scenes classification from VHR satellite imagery.Scientific data · 2026Article
- Vectorized Agrivoltaics Dataset in China from 2010 to 2022.Scientific data · 2026Article
- CPVPD-2024: A Chinese photovoltaic plant dataset derived via a topography-enhanced deep learning framework.Scientific data · 2025Article
- A harmonized dataset of ground-mounted solar energy in the US with enhanced metadata.Scientific data · 2025Article
- Satellite-based analysis uncovers uneven solar PV distribution across Japan and its consumption of forest and agricultural lands.Scientific reports · 2025Article
- Ecological dichotomies of solar energy expansion: resilience in arid regions versus fragility in humid ecosystems.Frontiers in plant science · 2025Article
- Vectorized solar photovoltaic installation dataset across China in 2015 and 2020.Scientific data · 2024Article
- A 10-m scale chemical industrial parks map along the Yangtze River in 2021 based on machine learning.Scientific data · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
We provide a remote sensing derived dataset for large-scale ground-mounted photovoltaic (PV) power stations in China of 2020, which has high spatial resolution of 10 meters. The dataset is based on the Google Earth Engine (GEE) cloud computing platform via random forest classifier and active learning strategy. Specifically, ground samples are carefully collected across China via both field survey and visual interpretation. Afterwards, spectral and texture features are calculated from publicly available Sentinel-2 imagery. Meanwhile, topographic features consisting of slope and aspect that are sensitive to PV locations are also included, aiming to construct a multi-dimensional and discriminative feature space. Finally, the trained random forest model is adopted to predict PV power stations of China parallelly on GEE. Technical validation has been carefully performed across China which achieved a satisfactory accuracy over 89%. Above all, as the first publicly released 10-m national-scale distribution dataset of China's ground-mounted PV power stations, it can provide data references for relevant researchers in fields such as energy, land, remote sensing and environmental sciences.
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Registered trials
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