Efficient classification and segmentation of five photovoltaic types (GFTPV, GSATPV, RPV, FPV and SPV) have been realized by PV-CSN, and more accurate and detailed photovoltaic data have been obtained.
Does China have a spatial map of PV power stations?
Although some researchers released several PV power station maps, most only met a medium resolution of 30 meters 9, 10. There thus still lacks a national map of China's PV power stations with a higher spatial resolution (i.e., 10 meters) that could provide a global understanding of PV's spatial deployment patterns.
Does China have a solar power plant?
China's newly installed photovoltaic capacity has ranked first in the world in recent years. Timely and accurate monitoring of the spatiotemporal distribution characteristics of solar power plants is essential to optimize China's renewable energy power distribution and achieve carbon reduction targets.
Does China have a PV power plant?
When looking into the publicly released scientific data of China's PV power stations, only the statistical data of PV's installed capacity for each province could be achieved, lacking the spatial distribution data that could provide more details of China's PV power industry.
How big is China's PV power station?
China's total PV power station area in 2020 was estimated as 2635.64 km 2. China's PV power generation in 2020 was calculated to be 238.65 TWh. This power amount is equivalent to reducing carbon emissions by 149.63 million tons. Evaluation results favor Sustainable Development Goals and carbon neutrality.
What is remote sensing derived dataset for large-scale photovoltaic power stations in China?
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.
How do we classify PV power plants from composite images?
We applied a pixel-based random forest (RF) model to classify the PV power plants from composite images in 2020 with a 30 m spatial resolution on the Google Earth Engine (GEE). The resulting classification map was further improved by a visual interpretation approach.