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 19,433 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.<br><i>Note: this version of the dataset has been improved to increase the accuracy of polygon georeferencing so that the data can be more easily integrated with imagery other than than the original imagery from which the annotations were based. Additionally, a small number of polygons were found to be erroneous annotations and either corrected or removed.</i>

对地观测遥感数据（Earth-observing remote sensing data）包含航空摄影与卫星影像，可为全球提供快照式观测视角，助力我们解析环境现状、人为活动系统与自然资源状况。结合机器学习方法，可通过此类遥感数据对可从空中观测到的能源系统组分开展评估。本研究聚焦分布式太阳能光伏（Solar Photovoltaic，PV）阵列相关的数据缺口——当前小地理尺度下的太阳能光伏部署情况公开数据十分有限。为此我们构建了一套机器学习数据集，用于研发基于遥感影像自动识别太阳能光伏阵列位置的流程。
该数据集涵盖美国加利福尼亚州4座城市的601幅高分辨率影像中的19433块太阳能光伏板的地理空间坐标与边界顶点信息。本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、研发面向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4座城市的航空影像下载链接可参见参考文献。
*注：本数据集版本已完成优化，提升了多边形地理配准的精度，以便该数据可更便捷地与标注来源之外的其他影像进行整合。此外，本数据集修正或移除了少量存在标注错误的多边形。*

搜集汇总

背景与挑战

背景概述

该数据集包含加利福尼亚州四个城市中19,433个太阳能电池板的地理坐标和边界顶点，覆盖601张高分辨率图像，适用于遥感图像识别和机器学习算法训练。数据集还支持分布式光伏系统的预测检测和社会经济相关性分析。