datasetposted on 2021-01-08, 09:28 authored by Duke Bass Connections Deep Learning for Rare Energy Infrastructure 2020-2021Duke Bass Connections Deep Learning for Rare Energy Infrastructure 2020-2021
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.
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.