Distributed Solar Photovoltaic Array Location and Extent Data Set for Remote Sensing Object Identification

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of our environment, anthropogenic systems, and natural resources. The components of energy systems that are visible from above may be assessed with these remote sensing data when combined with machine learning methods. Here we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited data on solar PV deployments at small geographic scales. We created a machine learning dataset to develop the process of automatically identifying solar PV locations through the use of remote sensing imagery.<br>This dataset contains the geospatial coordinates and border vertices for 19,863 solar panels across 601 high resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, and analysis of the socioeconomic correlates of PV deployment.<br>Links to the aerial photographs from Fresno, Stockton, Oxnard, and Modesto can be found in the references.对地观测遥感数据（包括航空摄影与卫星影像）可为我们呈现全球全景快照，借此可深入了解环境现状、人类活动系统与自然资源状况。结合机器学习方法，可借助此类遥感数据对从高空可见的能源系统组成部分进行评估。本研究聚焦于分布式太阳能光伏阵列（distributed solar photovoltaic (PV) arrays）相关的信息缺口——当前小地理尺度下的太阳能光伏部署数据极为匮乏。为此我们构建了一套机器学习数据集，用于开发基于遥感影像自动识别太阳能光伏阵列位置的流程。 本数据集涵盖美国加利福尼亚州四座城市的601张高分辨率影像中的19863块太阳能光伏板的地理空间坐标与边界顶点信息。该数据集的应用场景包括：训练基于遥感影像的目标检测及其他机器学习算法、开发用于分布式光伏系统预测性检测的专用算法，以及分析光伏部署与社会经济因素的相关性。 弗雷斯诺、斯托克顿、奥克斯纳德与莫德斯托的航空影像链接可参见参考文献。