Photovoltaic Power Station Identification Based on High-Resolution Network and Google Earth Engine: A Case Study of Qinghai Province, Northwest China

The precise identification of photovoltaic power stations is essential for advancing the assessment of energy infrastructure and for the efficient management of land resources. To address the need for spatially explicit data on photovoltaic (PV) development in arid and semi-arid regions amid green energy transitions, particularly in the context of identification challenges induced by the widespread distribution of bare ground, this study optimized a remote sensing-based identification method integrating Principal Component Analysis (PCA), automated sampling via Google Earth Engine (GEE), and deep learning models, and applied it to Qinghai Province, one of China’s largest PV regions. The results showed that HRNetv2 (validation Dice = 0.9463) outperformed UNet (0.9328), Attention UNet (0.9399), and HRNet + OCR (0.9184) in small-sample (1871 training samples) PV segmentation; the PV installed area during 2020–2024 accounted for 63.5% of the total pre-2024 area (~607 km2), exceeding the cumulative area before 2019, with projects predominantly distributed in areas with elevation less than 2500 m and slope less than 2°; bare land dominated PV land use (88.7%), followed by grassland (6.9%) and shrubland (3.9%), and PV construction contributed to desert greening by modifying microclimates. The study concludes that its optimized method effectively supports PV spatial identification, and the revealed PV distribution and land use patterns provide scientific guidance for synergistic PV development and ecological conservation in arid regions, while acknowledging limitations in generalizability to other regions due to Qinghai-specific data, suggesting future algorithm refinement and expanded research scales.