Baseline Dataset

Overview
This is a set of overhead images of
wind turbines with corresponding YOLOv3 formatted labels for object
detection. These labels contain the class, x and y coordinates and the
height and width of the bounding boxes for each wind turbine in the
corresponding image.
Why
Deep
learning can help with the analysis of energy infrastructure. Extending
this work to more types of energy infrastructure can create a pipeline
for in-depth energy infrastructure analysis that could provide
information for energy access decision makers to choose how to provide
electricity to a non-electrified region (through grid extension,
micro-grids or localized power generation).

MethodThe
majority of the images were taken from
https://figshare.com/articles/Power_Plant_Satellite_Imagery_Dataset/5307364.
These images were then hand labeled and converted into formatted
labels, which are also contained in original_images_and_labels. This
data was then preprocessed into smaller images with dimensions of
608x608 and their corresponding labels with the same YOLOv3 format of
class, x, y, height, width. These values (except for class value) have relative values from 0-1 that are proportional to the size of the images. These smaller images and labels are what are contained in the dataset. These images have resolutions varying from 0.6-1m.
Additional images were collected through the NAIP imagery available on Earth OnDemand and then hand-labeled.

概述
本数据集包含若干航拍风力涡轮机图像，以及适配目标检测任务的YOLOv3格式标注文件。每份标注文件均包含对应图像中每台风力涡轮机的类别、边界框的x、y坐标及高宽信息。

研究动因
深度学习可助力能源基础设施分析工作。若将该方法拓展至更多类型的能源基础设施，可构建一套面向能源基础设施的深度分析流程，为能源接入决策制定者提供参考，以辅助其选择向无电区域供电的方案（包括电网延伸、微电网建设或本地化发电）。

数据处理方法
本数据集的大部分图像源自https://figshare.com/articles/Power_Plant_Satellite_Imagery_Dataset/5307364。原始图像经人工标注并转换为标准化标注文件，相关内容亦收纳于original_images_and_labels文件夹中。随后，该数据集被预处理为尺寸608×608的子图像，以及与之匹配的YOLOv3格式标注文件，标注内容仍包含类别、x、y坐标及边界框高宽。除类别值外，其余坐标与尺寸参数均为0至1范围内的相对值，与图像实际尺寸成比例。本数据集所包含的正是这些经过预处理的子图像与对应标注文件，原始图像的分辨率介于0.6至1米之间。
此外，本数据集还通过Earth OnDemand平台提供的NAIP影像采集，并经人工标注完成。