论文标题
印度太阳能位置的人工智能数据集
An Artificial Intelligence Dataset for Solar Energy Locations in India
论文作者
论文摘要
可再生能源的快速发展，尤其是太阳能光伏（PV），对于减轻气候变化至关重要。结果，印度设定了雄心勃勃的目标，可以到2030年安装500吉瓦的太阳能容量。鉴于预计大量的占地面积可以满足可再生能源的能源目标，因此，土地利用冲突的潜力高于环境价值。为了加快太阳能的发展，土地使用计划者将需要访问PV基础设施的最新，准确的地理空间信息。在这项工作中，我们开发了一种具有空间显式的机器学习模型，以使用自由使用的卫星图像绘制印度的公用事业规模的太阳能项目，平均精度为92％。我们的模型预测得到了人类专家的验证，以获取1363个太阳能光伏农场的数据集。使用此数据集，我们测量了整个印度的太阳足迹，并量化了与PV基础设施发展相关的陆生修改程度。我们的分析表明，印度超过74％的太阳能开发是建立在具有自然生态系统保护或农业价值的陆生类型上的。
Rapid development of renewable energy sources, particularly solar photovoltaics (PV), is critical to mitigate climate change. As a result, India has set ambitious goals to install 500 gigawatts of solar energy capacity by 2030. Given the large footprint projected to meet renewables energy targets, the potential for land use conflicts over environmental values is high. To expedite development of solar energy, land use planners will need access to up-to-date and accurate geo-spatial information of PV infrastructure. In this work, we developed a spatially explicit machine learning model to map utility-scale solar projects across India using freely available satellite imagery with a mean accuracy of 92%. Our model predictions were validated by human experts to obtain a dataset of 1363 solar PV farms. Using this dataset, we measure the solar footprint across India and quantified the degree of landcover modification associated with the development of PV infrastructure. Our analysis indicates that over 74% of solar development In India was built on landcover types that have natural ecosystem preservation, or agricultural value.
