Real and Synthetic Overhead Images of Wind Turbines in the US

OverviewThis dataset contains real overhead images of wind turbines in the US collected through the National Agriculture Imagery Plan (NAIP), as well as synthetic overhead images of wind turbines created to be similar to the real images. All of these images are 608x608. For more details on the methodology and data, please read the sections below, or look at our website: Locating Energy Infrastructure with Deep Learning (duke-bc-dl-for-energy-infrastructure.github.io). Real DataThe real data consists of images.zip and labels.zip. There are 1,742 images in images.zip, and for each image in this folder, there is a corresponding label with the same name, but a different extension. Some images do not have labels, meaning there are no wind turbines in those images. Many of these overhead images of wind turbines were collected from Power Plant Satellite Imagery Dataset (figshare.com) and then hand labeled. Others were collected using Google Earth Engine or EarthOnDemand and then labeled. All of the images collected are from the National Agricultural Imagery Program (NAIP), and all are 608x608 pixels. The labels are in YOLOv3 format, meaning each line in the text file corresponds with one wind turbine. Each line is formatted as: class x_center y_center width height. Since there is only one class, class is always zero, and the x, y, width, and height are relative to the size of the image and are between 0-1. The image_locations.csv file contains the latitude and longitude for each image. It also contains the image's geographic domain that we defined. Our data comes from what we defined as four regions - Northeast (NE), Eastern Midwest (EM), Northwest (NW), and Southwest (SW), and these are included in the csv file for each image. These regions are defined by groups of states, so any data in WA, ID, or MT would be in the Northwest region. Synthetic DataThe synthetic data consists of synthetic_images.zip and synthetic_labels.zip. These images and labels were automatically generated using CityEngine. Again, all images are 608x608, and the format of the labels is the same. There are 943 images total, and at least 200 images for each of the four geographic domains that we defined in the US (Northwest, Southwest, Eastern Midwest, Northeast). The generation of these images consisted of the software selecting a background image, then generating 3D models of turbines on top of that background image, and then positioning a simulated camera overhead to capture an image. The background images were collected nearby the locations of the testing images. ExperimentationOur Duke Bass Connections 2020-2021 team performed many experiments using this data to test if the synthetic imagery could help the performance of our object detection model. We designed experiments where we would have a baseline dataset of just real imagery, train and test an object detection model on it, and then add in synthetic imagery into the dataset, train the object detection model on the new dataset, and then compare it's performance with the baseline. For more information on the experiments and methodology, please visit our website here: Locating Energy Infrastructure with Deep Learning (duke-bc-dl-for-energy-infrastructure.github.io).本数据集包含通过美国国家农业影像计划（National Agriculture Imagery Plan，NAIP）采集的美国境内风力发电机组真实航拍影像，以及与真实影像风格匹配的合成航拍风力发电机组影像。所有影像分辨率均为608×608像素。如需了解方法学与数据的更多细节，请参阅下文章节，或访问我们的网站：《基于深度学习的能源基础设施定位》（duke-bc-dl-for-energy-infrastructure.github.io）。 真实数据包含images.zip与labels.zip两个压缩包。images.zip内共有1742张影像，该文件夹内的每张影像均对应一个同名但扩展名不同的标注文件。部分影像无对应标注文件，意味着其中未包含风力发电机组。这批风力发电机组航拍影像中，多数源自figshare平台的《发电厂卫星影像数据集》并经人工标注；其余影像通过Google Earth Engine或EarthOnDemand采集，随后完成标注。所有采集的影像均来自国家农业影像计划（National Agricultural Imagery Program，NAIP），分辨率均为608×608像素。标注文件采用YOLOv3格式，即文本文件内的每一行对应一台风力发电机组。每行格式为：class x_center y_center width height。由于仅存在单一类别，class字段始终为0；x、y、width及height均相对于影像尺寸归一化，取值范围为0至1。 image_locations.csv文件包含每张影像的经纬度坐标，同时记录了我们定义的影像地理区域。我们将数据划分为四大区域——东北部（Northeast，NE）、中西部东部（Eastern Midwest，EM）、西北部（Northwest，NW）与西南部（Southwest，SW），该信息已包含在每张影像对应的csv条目内。上述区域按州群划分，例如华盛顿州（WA）、爱达荷州（ID）或蒙大拿州（MT）的所有数据均属于西北部区域。 合成数据包含synthetic_images.zip与synthetic_labels.zip两个压缩包。这些影像与标注文件均通过CityEngine自动生成。同样，所有影像分辨率均为608×608像素，标注文件格式与真实数据一致。合成数据集总计包含943张影像，且我们定义的美国四大地理区域（西北部、西南部、中西部东部、东北部）各自至少包含200张影像。影像生成流程为：软件首先选取背景影像，随后在背景上生成风力发电机组的3D模型，再通过模拟的俯拍相机获取影像。背景影像采集自测试影像周边区域。 我们的杜克大学Bass Connections 2020-2021年度团队利用该数据集开展了大量实验，以验证合成影像能否提升目标检测模型的性能。实验设计方案为：首先仅使用真实影像作为基准数据集，在其上训练并测试目标检测模型；随后向数据集内加入合成影像，在新数据集上重新训练目标检测模型，并将其性能与基准模型进行对比。如需了解实验与方法学的更多信息，请访问我们的网站：《基于深度学习的能源基础设施定位》（duke-bc-dl-for-energy-infrastructure.github.io）。