Photovoltaic (PV) power, as a promising renewable energy technology, plays a pivotal role in achieving carbon neutrality. Accurately locating PV power plants and tracking their spatiotemporal expansion are essential for informed energy policymaking, industrial planning, and climate change mitigation. 

However, with the rapid global surge in PV installations, existing geospatial datasets suffer from significant limitations, including restricted spatial coverage, insufficient resolution, and a lack of timely updates, which hinders the current research and practical needs.

In a study published in International Journal of Applied Earth Observation and Geoinformation, a research team led by Assoc. Prof. SUN Liqun from the Shenzhen Institute of Advanced Technology (SIAT) of the Chinese Academy of Sciences developed a 10-meter resolution global dataset of centralized PV power plants.

Researchers proposed an "adaptive index + multi-source data fusion" framework. They integrated three datasets, i.e., TZ-SAM for global localization, ChinaPV for 10 m high-precision mapping in China, and GlobalPV for global time-series information. 

Besides, researchers developed the Adaptive Normalized Difference Photovoltaic Index (ANDPI), which automatically separates PV from non-PV surfaces using bimodal distribution thresholding, and they built an efficient fusion pipeline that optimizes historical data while updating the latest dynamics, thus producing global PV mapping results at 10m resolution.

This comprehensive global dataset reveals clear patterns in the evolution of clean energy development. It also highlights a rationalizing trend in land use. This study highlights more balanced PV siting practices, and demonstrates the low-cost scalability of multi-source data fusion for monitoring other renewables.