A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020

We provide a remote sensing classification 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 using random forest classifier. Specifically, ground samples are carefully collected around China via both field survey and visual interpretation. Afterwards, spectral and texture features are calculated together with topographic features that are sensitive to PV locations including slope and aspect, aiming to construct a multi-dimensional feature space. Finally, the trained random forest model is adopted predict PV power stations of China on GEE. 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.