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

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

SUBMITTER: Feng Q 

PROVIDER: S-EPMC10864270 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature