UAV-based solar photovoltaic detection dataset

This dataset contains unmanned aerial vehicle (UAV) imagery (a.k.a. drone imagery) and annotations of solar panel locations captured from controlled flights at various altitudes and speeds across two sites at Duke Forest (Couch field and Blackwood field). In total there are 423 stationary images and corresponding annotations of solar panels within sight, along with 60 videos taken from flying the UAV roughly at either 8 m/s or 14 m/s. In total there are 2,019 solar panel instances annotated. Associated publication: “Utilizing geospatial data for assessing energy security: Mapping small solar home systems using unmanned aerial vehicles and deep learning” [https://arxiv.org/abs/2201.05548] Data processing: Please refer to this Github repository for further details on data management and preprocessing: https://github.com/BensonRen/Drone_based_solar_PV_detection. The two scripts included enable the user to reproduce the experiments in the paper above. Contents: After unzipping the package, there will be 3 directories: 1. Train_val_set: Stationary UAV images (.JPG) taken at various altitudes in the Couch field of Duke Forest for training and validation purposes, along with their solar PV annotations (.png) 2. Test_set: Stationary UAV images (.JPG) taken at various altitudes in the Blackwood field of Duke Forest for test purposes, along with their solar PV annotations (.png) 3. Moving_labeled: Images (img/*.png) capture from videos moving with two speed modes (Sport: 14m/s, Norma: 8m/s) at various altitudes and their solar PV annotations (labels/*.png) For additional details of this dataset, please refer to REAMDE.docx enclosed. Acknowledgments: This dataset was created at the Duke University Energy Initiative in collaboration with the Energy Access Project at Duke and RTI International. We thank the Duke University Energy Data Analytics Ph.D. Student Fellowship Program for their support. We also thank Duke Forest for use of the flight zones for data collection.

本数据集包含无人机（Unmanned Aerial Vehicle, UAV）航拍影像及光伏板位置标注数据，采集自杜克森林两处场地（Couch场与Blackwood场）中，于不同高度、速度下开展的受控飞行任务所拍摄的内容。
本数据集总计包含423张静态图像及对应可视范围内光伏板的标注文件，同时配有60段由无人机以约8 m/s或14 m/s的飞行速度拍摄的视频，总计标注了2019个光伏板实例。
相关学术论文：《利用地理空间数据评估能源安全：基于无人机与深度学习的小型家用光伏系统制图》[https://arxiv.org/abs/2201.05548]
数据处理相关细节请参阅本GitHub仓库：https://github.com/BensonRen/Drone_based_solar_PV_detection，仓库内附带的两份脚本可复现上述论文中的实验。
数据集内容说明：解压数据包后，将得到3个目录：
1. Train_val_set（训练验证集）：包含在杜克森林Couch场不同高度拍摄的静态无人机航拍图像（格式为.JPG），用于模型训练与验证，同时附带对应的光伏板标注文件（格式为.PNG）
2. Test_set（测试集）：包含在杜克森林Blackwood场不同高度拍摄的静态无人机航拍图像（格式为.JPG），用于模型测试，同时附带对应的光伏板标注文件（格式为.PNG）
3. Moving_labeled（带标注的移动拍摄数据集）：包含以两种飞行速度模式（Sport模式：14 m/s，Norma模式：8 m/s）在不同高度飞行时拍摄的视频帧图像（存储路径为img/*.png），以及对应的光伏板标注文件（存储路径为labels/*.png）
如需了解本数据集的更多细节，请参阅随包附带的REAMDE.docx文件。
致谢声明：本数据集由杜克大学能源倡议组织与杜克大学能源获取项目及RTI国际组织合作创建。我们感谢杜克大学能源数据分析博士生奖学金计划的资助，同时感谢杜克森林允许使用其飞行区域开展数据采集工作。