全球光伏太阳能电池板数据集从2019年到2022年
Anqi Li1, Luling Liu1, Shijie Li1
1State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
Scientific data
|April 16, 2025
概括
使用先进的人工智能创建了一个新的全球太阳能光伏 (PV) 面板数据集 (2019-2022年). 这些高分辨率数据显示了超过60%的光伏增长,发展中国家推动了扩张.
科学领域:
- 可再生能源系统可再生能源系统
- 地理空间数据科学数据科学
- 环境监测中的人工智能
背景情况:
- 太阳能光伏 (PV) 电力对全球能源至关重要,但缺乏精确,最新的空间数据集.
- 现有的数据限制阻碍了对光伏潜力和增长的有效空间分析.
- 精确的全球光伏测绘对于能源政策和能源研究至关重要.
研究的目的:
- 开发一种用于全球光伏面板识别的新方法.
- 从2019-2022年创建一个高分辨率 (20米) 的光伏电池板的年度数据集.
- 为空间分析和政策制定提供详细和准确的资源.
主要方法:
- 一个两阶段的PV分类框架,结合了U-Net和积极的未标记的学习与随机森林 (PUL-RF).
- U-Net从谷歌地球的分米图像中识别了PV,以生成初始的阳性样本.
- PUL-RF利用这些样本来对Sentinel-2的大规模图像进行分类,用于全球光伏映射.
主要成果:
- 使用IoU和F1-Score评估指标实现了90%以上的准确性.
- 与现有的全球光伏数据集相比,生成的数据集提供了更高的精度和空间细节.
- 记录了在2019年至2022年期间全球光伏容量增长超过60%的显著增长.
结论:
- 开发的方法和数据集为全球光伏分析提供了前所未有的细节.
- 发展中国家正在为太阳能光伏部署的快速增长做出重大贡献.
- 该资源将支持未来的研究,能源规划和气候变化减缓战略.
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