Wind Turbine Detection (by Saurabh Shahane)

Introduction #

Saurabh Shahane, Kyle Bradbury, Benjamin Brigmanet al.

The author of the Wind Turbine Detection dataset primarily sourced the majority of images from the Power Plant Satellite Imagery Dataset. Subsequently, they engaged in manual labeling of the images and converted them into properly formatted labels, which are also included in the folder named original_images_and_labels. After this data preparation, they performed preprocessing to generate smaller images with dimensions of 608x608 pixels, along with their corresponding labels formatted in YOLOv3 format, including class, x, y, height, and width. The values for x, y, height, and width are relative and range from 0 to 1, proportionally scaled to the size of the images. This dataset comprises these smaller images and labels, with image resolutions varying between 0.6-1m.

The original Power Plant Satellite Imagery Dataset contains satellite imagery of 4,454 power plants within the United States. The imagery is provided at two resolutions: 1m (4-band NAIP iamgery with near-infrared) and 30m (Landsat 8, pansharpened to 15m). The NAIP imagery is available for the U.S. and Landsat 8 is available globally. This dataset may be of value for computer vision work, machine learning, as well as energy and environmental analyses.

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Homepage

Summary #

Wind Turbine Detection 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 1742 images with 3889 labeled objects belonging to 1 single class (wind turbine).

Images in the Wind Turbine Detection (by Saurabh Shahane) dataset have bounding box annotations. There are 457 (26% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2020.

Here is the visualized example grid with annotations:

Explore #

Wind Turbine Detection (by Saurabh Shahane) dataset has 1742 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.

Sample annotation mask from Wind Turbine Detection (by Saurabh Shahane)

Sample image from Wind Turbine Detection (by Saurabh Shahane)

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Have a look at 1742 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.

Rows 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
wind turbine➔ rectangle	1285	3889	3.03	1.12%

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.

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	3889	0.37%	6.5%	0.01%	6px	0.99%	191px	31.41%	27px	4.49%	7px	1.15%	208px	34.21%

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.

Objects #

Table contains all 3889 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.

Rows 1-10 of 3889

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	wind turbine rectangle	naip_4147_MI_WND_i0j1.jpg	608 x 608	47px	7.73%	48px	7.89%	0.61%
2➔	wind turbine rectangle	naip_7376_TX_WND_i1j0.jpg	608 x 608	45px	7.4%	69px	11.35%	0.84%
3➔	wind turbine rectangle	naip_1751_CA_WND_i0j0.jpg	608 x 608	35px	5.76%	53px	8.72%	0.5%
4➔	wind turbine rectangle	naip_1751_CA_WND_i0j0.jpg	608 x 608	32px	5.26%	53px	8.72%	0.46%
5➔	wind turbine rectangle	naip_1751_CA_WND_i0j0.jpg	608 x 608	35px	5.76%	51px	8.39%	0.48%
6➔	wind turbine rectangle	naip_3305_KS_WND_i1j1.jpg	608 x 608	24px	3.95%	55px	9.05%	0.36%
7➔	wind turbine rectangle	naip_1266_CA_WND_i1j0.jpg	608 x 608	28px	4.61%	65px	10.69%	0.49%
8➔	wind turbine rectangle	naip_1266_CA_WND_i1j0.jpg	608 x 608	23px	3.78%	60px	9.87%	0.37%
9➔	wind turbine rectangle	naip_1266_CA_WND_i1j0.jpg	608 x 608	20px	3.29%	61px	10.03%	0.33%
10➔	wind turbine rectangle	naip_2779_ID_WND_i1j0.jpg	608 x 608	46px	7.57%	60px	9.87%	0.75%

License #

Wind Turbine Detection is under CC BY 4.0 license.

Source

Citation #

If you make use of the Wind Turbine Detection (by Saurabh Shahane) data, please cite the following reference:

@dataset{Wind Turbine Detection (by Saurabh Shahane),
	author={Saurabh Shahane and Kyle Bradbury and Benjamin Brigman and Gouttham Chandrasekar and Leslie Collins and Shamikh Hossain and Marc Jeuland and Timothy Johnson and Boning Li and Trishul Nagenalli},
	title={Wind Turbine Detection},
	year={2020},
	url={https://www.kaggle.com/datasets/saurabhshahane/wind-turbine-obj-detection}
}

Source

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

@misc{ visualization-tools-for-wind-turbine-detection-dataset,
  title = { Visualization Tools for Wind Turbine Detection (by Saurabh Shahane) Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/wind-turbine-detection } },
  url = { https://datasetninja.com/wind-turbine-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Dataset Wind Turbine Detection (by Saurabh Shahane) 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='Wind Turbine Detection (by Saurabh Shahane)', 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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