CnOpenData Global Photovoltaic and Wind Turbine Data is a foundational spatial dataset covering global commercial photovoltaic power stations and onshore wind turbines, generated through high-resolution image interpretation. Its core output, in standardized image formats, clearly delineates the geographical locations and spatial extents of over 375,000 wind turbines and 86,000 photovoltaic power stations worldwide. All facilities have been precisely georeferenced to form a base map directly applicable for visualization and macro-level analysis. This dataset provides rare, consistent, and comparable foundational data support for quantitatively analyzing the construction scale, geographical pattern evolution, and land occupation of global renewable energy infrastructure, serving as a valuable resource for understanding the actual progress of global energy transition.
Data Features
- High-precision Spatial Basemap: Generated through standardized interpretation of high-quality imagery, the data provides verifiable and comparable spatial location benchmarks for renewable energy facilities worldwide.
- Flexible Scalability: Provided in universal geographic information formats, the data seamlessly integrates with mainstream GIS platforms and various external data sources (e.g., administrative boundaries, terrain, socioeconomic statistics), facilitating multi-source data fusion and complex spatial analysis.
- Support for Customized Deep Mining: Beyond basic spatial locations, we offer professional deep-processing services tailored to specific research objectives, such as extracting facility clusters, calculating spatial density, and generating aggregated data for specific statistical units, directly delivering analysis-ready data products.
Data Application Value
- Serve Energy Planning and Policy Evaluation: Provide government agencies and research institutions with intuitive global and regional distribution basemaps of wind and solar facilities, supporting resource potential assessment, infrastructure planning, and policy effectiveness analysis.
- Support Interdisciplinary Academic Research: Offer high-quality spatial baselines for research in energy geography, land science, environmental economics, etc., facilitating cross-disciplinary studies such as site selection analysis, driving factors, and environmental/social impacts when combined with other data.
- Empower Industry Analysis and Business Decisions: Provide infrastructure maps for energy companies and investment institutions, supporting preliminary site screening, market competition analysis, and customized deep data analysis for specific business scenarios.
This database comprehensively presents the spatial distribution of global wind and solar power facilities, serving as a direct tool for macro-level analysis and a structured data foundation for in-depth research, thereby providing reliable spatial data support for energy transition-related studies, planning, and decision-making.
Time Range
Q4 2017 to Q2 2024