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Large Wind Turbines (by Duke Dataplus2020) Dataset

44111870
Tagenergy-and-utilities, aerial, satellite
Taskobject detection
Release YearMade in 2020
LicenseCC BY 4.0
Download305 MB

Introduction #

The authors of the dataset present a collection of synthetic overhead imagery of wind turbines, which was generated using CityEngine. For each image, corresponding labels are provided in the YOLOv3 format, containing essential information such as the class, x and y coordinates, and height and width of the ground truth bounding boxes for each wind turbine.

The motivation behind creating this dataset was to address the challenge of acquiring sufficient data for training object detection models on wind turbines. Since wind turbines are rare and sparse, gathering data can be costly. The authors propose using synthetic imagery to automate the generation of new training data, thereby mitigating the scarcity of real-world samples. Furthermore, the use of synthetic imagery can help with cross-domain testing, where the model’s performance may suffer due to limited training data from certain regions.

To generate the dataset, the authors selected background images from Power Plant Satellite Imagery Dataset, ensuring that they were not part of the existing “Overhead Imagery of Wind Turbines” dataset and lacked excessive infrastructure that might render the scene unrealistic. Subsequently, a script was employed to randomly and uniformly generate 3D models of large wind turbines over the selected background images, and the virtual camera was positioned to save four 608x608 pixel images. This process was repeated with a consistent random seed, but this time the background image was omitted, and the wind turbines were colored in black. The resulting black and white images were then converted into ground truth labels by grouping the black pixels to represent the wind turbines’ locations.

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Dataset LinkHomepage

Summary #

Synthetic Overhead Images and Ground Truth Labels of Large 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 441 images with 2334 labeled objects belonging to 1 single class (wind turbine).

Images in the Large Wind Turbines (by Duke Dataplus2020) dataset have bounding box annotations. There is 1 unlabeled image (i.e. without annotations). There is 1 split in the dataset: train (441 images). The dataset was released in 2020 by the Duke Dataplus2020.

Here is the visualized example grid with annotations:

Explore #

Large Wind Turbines (by Duke Dataplus2020) dataset has 441 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 Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
OpenSample annotation mask from Large Wind Turbines (by Duke Dataplus2020)Sample image from Large Wind Turbines (by Duke Dataplus2020)
👀
Have a look at 441 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 turbine
rectangle
440
2334
5.3
3.42%

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.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

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 turbine
rectangle
2334
0.65%
2.62%
0.08%
13px
2.14%
134px
22.04%
51px
8.38%
14px
2.3%
127px
20.89%

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 2334 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 2334
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
wind turbine
rectangle
wnd_xview_bkg_sd16_3.png
608 x 608
58px
9.54%
17px
2.8%
0.27%
2
wind turbine
rectangle
wnd_xview_bkg_sd18_4.png
608 x 608
30px
4.93%
43px
7.07%
0.35%
3
wind turbine
rectangle
wnd_xview_bkg_sd18_4.png
608 x 608
78px
12.83%
61px
10.03%
1.29%
4
wind turbine
rectangle
wnd_xview_bkg_sd18_4.png
608 x 608
47px
7.73%
65px
10.69%
0.83%
5
wind turbine
rectangle
wnd_xview_bkg_sd18_4.png
608 x 608
71px
11.68%
79px
12.99%
1.52%
6
wind turbine
rectangle
wnd_xview_bkg_sd45_4.png
608 x 608
64px
10.53%
50px
8.22%
0.87%
7
wind turbine
rectangle
wnd_xview_bkg_sd45_4.png
608 x 608
92px
15.13%
38px
6.25%
0.95%
8
wind turbine
rectangle
wnd_xview_bkg_sd45_4.png
608 x 608
22px
3.62%
23px
3.78%
0.14%
9
wind turbine
rectangle
wnd_xview_bkg_sd45_4.png
608 x 608
60px
9.87%
27px
4.44%
0.44%
10
wind turbine
rectangle
wnd_xview_bkg_sd45_4.png
608 x 608
45px
7.4%
21px
3.45%
0.26%

License #

Synthetic Overhead Images and Ground Truth Labels of Large Wind Turbines is under CC BY 4.0 license.

Source

Citation #

If you make use of the Wind Turbines 4 data, please cite the following reference:

@article{Duke Dataplus20202020,
    author = "Duke Dataplus2020",
    title = "{Synthetic Overhead Images and Ground Truth Labels of Large Wind Turbines}",
    year = "2020",
    month = "7",
    url = "https://figshare.com/articles/dataset/Synthetic_Overhead_Images_and_Ground_Truth_Labels_of_Large_Wind_Turbines/12744902",
    doi = "10.6084/m9.figshare.12744902.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-synthetic-overhead-images-and-ground-truth-labels-of-large-wind-turbines-dataset,
  title = { Visualization Tools for Large Wind Turbines (by Duke Dataplus2020) Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/synthetic-overhead-images-and-ground-truth-labels-of-large-wind-turbines } },
  url = { https://datasetninja.com/synthetic-overhead-images-and-ground-truth-labels-of-large-wind-turbines },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-01 },
}

Download #

Dataset Large 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='Large 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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