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Baseline Dataset
dataset
posted 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 2020-2021Overview
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).
Method
The
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.
