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Overhead Imagery of Wind Turbines (by Duke Dataplus2020) Dataset

206511
Tagenergy-and-utilities, aerial, satellite
Taskobject detection
Release YearMade in 2020
LicenseCC BY 4.0
Download271 MB

Introduction #

The authors of the dataset present a collection of overhead images of wind turbines along with corresponding YOLOv3 formatted labels for object detection. The labels provide essential information, such as the class, x and y coordinates, and the height and width of the bounding boxes for each wind turbine in the respective image.

The motivation behind this work is to explore the application of deep learning in the analysis of energy infrastructure. The authors aim to extend this approach to encompass various types of energy infrastructure, creating a comprehensive pipeline for in-depth energy infrastructure analysis. Such an analysis could offer valuable insights for energy access decision makers, enabling them to make informed choices on how to provide electricity to non-electrified regions, considering options like grid extension, micro-grids, or localized power generation.

For data acquisition, the images were sourced from the Power Plant Satellite Imagery Dataset. Subsequently, these images were carefully hand-labeled and transformed into properly formatted labels. The authors then performed data preprocessing, resizing the images to smaller dimensions of 608x608 and adjusting their corresponding labels accordingly, following the YOLOv3 format.

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Dataset LinkHomepage

Summary #

Overhead Imagery of Wind Turbines is a dataset for an object detection task. Possible applications of the dataset could be in the energy industry. The dataset presented here is not the original one. Learn more on the dataset’s homepage.

The dataset consists of 2065 images with 6396 labeled objects belonging to 1 single class (wind turbines).

Images in the Overhead Imagery of Wind Turbines (by Duke Dataplus2020) dataset have bounding box annotations. There are 512 (25% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: train (1652 images) and val (413 images). The dataset was released in 2020.

Here is the visualized example grid with annotations:

Explore #

Overhead Imagery of Wind Turbines (by Duke Dataplus2020) dataset has 2065 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)Sample image from Overhead Imagery of Wind Turbines (by Duke Dataplus2020)
👀
Have a look at 2065 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-1 of 1
Class
Images
Objects
Count on image
average
Area on image
average
wind turbines
rectangle
1553
6396
4.12
0.92%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-1 of 1
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
wind turbines
rectangle
6396
0.22%
3.06%
0%
5px
0.45%
97px
15.95%
25px
3.38%
5px
0.45%
142px
23.36%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 6396 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 6396
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
wind turbines
rectangle
naip_1812_CO_WND_i1j0.jpg
608 x 608
26px
4.28%
59px
9.7%
0.41%
2
wind turbines
rectangle
naip_4412_MN_WND_i1j0.jpg
608 x 608
16px
2.63%
87px
14.31%
0.38%
3
wind turbines
rectangle
naip_4105_MI_WND_i1j1.jpg
608 x 608
58px
9.54%
21px
3.45%
0.33%
4
wind turbines
rectangle
naip_4105_MI_WND_i1j1.jpg
608 x 608
53px
8.72%
19px
3.12%
0.27%
5
wind turbines
rectangle
naip_2963_IL_WND_i1j1.jpg
608 x 608
33px
5.43%
60px
9.87%
0.54%
6
wind turbines
rectangle
naip_664_CA_WND_i1j1.jpg
608 x 608
20px
3.29%
19px
3.12%
0.1%
7
wind turbines
rectangle
naip_664_CA_WND_i1j1.jpg
608 x 608
21px
3.45%
17px
2.8%
0.1%
8
wind turbines
rectangle
naip_664_CA_WND_i1j1.jpg
608 x 608
18px
2.96%
18px
2.96%
0.09%
9
wind turbines
rectangle
naip_1434_CA_WND_i1j0.jpg
608 x 608
14px
2.3%
18px
2.96%
0.07%
10
wind turbines
rectangle
naip_1434_CA_WND_i1j0.jpg
608 x 608
10px
1.64%
16px
2.63%
0.04%

License #

Overhead Imagery of Wind Turbines is under CC BY 4.0 license.

Source

Citation #

If you make use of the Wind Turbines 3 data, please cite the following reference:

@article{Duke Dataplus20202020,
    author = "Duke Dataplus2020",
    title = "{Overhead Imagery of Wind Turbines}",
    year = "2020",
    month = "7",
    url = "https://figshare.com/articles/dataset/Overhead_Imagery_of_Wind_Turbines/12744977",
    doi = "10.6084/m9.figshare.12744977.v1"
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-real-overhead-wind-turbines-dataset,
  title = { Visualization Tools for Overhead Imagery of Wind Turbines (by Duke Dataplus2020) Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/real-overhead-wind-turbines } },
  url = { https://datasetninja.com/real-overhead-wind-turbines },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-21 },
}

Download #

Dataset Overhead Imagery of Wind Turbines (by Duke Dataplus2020) can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Overhead Imagery of Wind Turbines (by Duke Dataplus2020)', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

. . .

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