Global Renewables Watch：一个源自卫星影像的太阳能与风能时间序列数据集
Global Renewables Watch: A Temporal Dataset of Solar and Wind Energy Derived from Satellite Imagery
AI总结：
本研究发布Global Renewables Watch全球可再生能源时间序列数据集，通过深度学习分割模型分析卫星影像识别光伏与风电设施，验证精度高，为可持续发展研究和政策制定提供支撑。
摘要：
我们提出了一个全面的全球商业太阳能光伏（PV）电站和陆上风电机组时间序列数据集，该数据集源自2017年第四季度至2024年第二季度按季度分析的高分辨率卫星影像。我们通过训练基于深度学习的分割模型来从卫星影像中识别这些可再生能源设施，随后将模型部署在覆盖全球的超过13万亿像素的影像上，构建了该数据集。对于每个检测到的设施，我们估算其建设日期和建设前的土地利用类型。该数据集为可持续发展目标的进展提供了关键洞见，也是政策制定者、研究人员和利益相关方评估并推广有效可再生能源部署策略的宝贵资源。我们最终的空间数据集包含375197台独立风电机组和86410处太阳能光伏设施。我们将预测结果汇总到国家层面，基于建设日期、太阳能光伏面积和风电机组数量估算总装机容量，结果显示，与IRENA最新的2023年国家级装机容量估算值相比，太阳能光伏和陆上风能的$r^2$值分别为0.96和0.93。
英文摘要：
We present a comprehensive global temporal dataset of commercial solar photovoltaic (PV) farms and onshore wind turbines, derived from high-resolution satellite imagery analyzed quarterly from the fourth quarter of 2017 to the second quarter of 2024. We create this dataset by training deep learning-based segmentation models to identify these renewable energy installations from satellite imagery, then deploy them on over 13 trillion pixels covering the world. For each detected feature, we estimate the construction date and the preceding land use type. This dataset offers crucial insights into progress toward sustainable development goals and serves as a valuable resource for policymakers, researchers, and stakeholders aiming to assess and promote effective strategies for renewable energy deployment. Our final spatial dataset includes 375,197 individual wind turbines and 86,410 solar PV installations. We aggregate our predictions to the country level -- estimating total power capacity based on construction date, solar PV area, and number of windmills -- and find an $r^2$ value of $0.96$ and $0.93$ for solar PV and onshore wind respectively compared to IRENA's most recent 2023 country-level capacity estimates.
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