Wind Turbine Detection (by Noah Vriese)

Introduction #

Released 2022-12-16 ·Noah Vriese

The author of the Wind Turbine Detection via YOLOv7 dataset developed a wind turbine object detection model using raw LANDSAT and NAIP satellite imagery. The model was based on a transfer learning approach from the state-of-the-art YOLOv7 architecture. The primary goal of this model was to automate on-shore U.S. wind turbine count estimations.

Currently, the U.S. Wind Turbine Database and other state-of-the-art databases that monitor wind turbine development in the United States offer exceptional accuracy but suffer from poor temporal resolution, typically updating quarterly. In contrast, the developed model, when combined with recent satellite imagery data, can provide more frequent and up-to-date estimates of on-shore wind resources in the U.S. This can be of great value for both foreign and domestic investors, as well as government officials, especially in regions undergoing ongoing wind turbine development.

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Summary #

Wind Turbine Detection via YOLOv7 is a dataset for an object detection task. Possible applications of the dataset could be in the energy industry.

The dataset consists of 5215 images with 10063 labeled objects belonging to 1 single class (turbine).

Images in the Wind Turbine Detection (by Noah Vriese) dataset have bounding box annotations. There are 1555 (30% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (4629 images), valid (415 images), and test (171 images). The dataset was released in 2022.

Explore #

Wind Turbine Detection (by Noah Vriese) dataset has 5215 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 Noah Vriese)

Sample image from Wind Turbine Detection (by Noah Vriese)

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Have a look at 5215 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
turbine➔ rectangle	3660	10063	2.75	1.78%

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
turbine rectangle	10063	0.65%	9.27%	0.01%	2px	0.31%	219px	34.22%	42px	6.61%	2px	0.31%	231px	36.09%

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 10063 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 10063

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➔	turbine rectangle	naip_7377_TX_WND_i0j1_jpg.rf.eee034b463785a6e2afccadf5b521ec4.jpg	640 x 640	27px	4.22%	36px	5.62%	0.24%
2➔	turbine rectangle	naip_7377_TX_WND_i0j1_jpg.rf.eee034b463785a6e2afccadf5b521ec4.jpg	640 x 640	33px	5.16%	55px	8.59%	0.44%
3➔	turbine rectangle	naip_6682_OR_WND_i1j0_jpg.rf.46bd86feda2c457ebef03eb8ae8d0937.jpg	640 x 640	27px	4.22%	31px	4.84%	0.2%
4➔	turbine rectangle	naip_7182_SD_WND_i1j0_jpg.rf.09302ea93d1dafda7154f654a3b24a7e.jpg	640 x 640	33px	5.16%	71px	11.09%	0.57%
5➔	turbine rectangle	naip_7182_SD_WND_i0j0_jpg.rf.643ce77880c30f3f982a3a4b13fdafc0.jpg	640 x 640	60px	9.38%	22px	3.44%	0.32%
6➔	turbine rectangle	naip_7182_SD_WND_i0j0_jpg.rf.643ce77880c30f3f982a3a4b13fdafc0.jpg	640 x 640	115px	17.97%	19px	2.97%	0.53%
7➔	turbine rectangle	naip_7182_SD_WND_i0j0_jpg.rf.643ce77880c30f3f982a3a4b13fdafc0.jpg	640 x 640	56px	8.75%	51px	7.97%	0.7%
8➔	turbine rectangle	naip_7182_SD_WND_i0j0_jpg.rf.643ce77880c30f3f982a3a4b13fdafc0.jpg	640 x 640	55px	8.59%	63px	9.84%	0.85%
9➔	turbine rectangle	naip_7182_SD_WND_i0j0_jpg.rf.643ce77880c30f3f982a3a4b13fdafc0.jpg	640 x 640	53px	8.28%	63px	9.84%	0.82%
10➔	turbine rectangle	m_4209449_sw_15_1_20170701_13_21_jpg.rf.37b3d938e804d0c2df9849d7979b5aa4.jpg	640 x 640	31px	4.84%	15px	2.34%	0.11%

License #

Wind Turbine Detection via YOLOv7 is under MIT license.

Source

Citation #

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

@dataset{Wind Turbine Detection (by Noah Vriese),
	author={Noah Vriese},
	title={Wind Turbine Detection via YOLOv7},
	year={2022},
	url={https://github.com/nvriese1/WindTurbineDetection}
}

Source

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

@misc{ visualization-tools-for-yolov7-wind-turbine-detection-dataset,
  title = { Visualization Tools for Wind Turbine Detection (by Noah Vriese) Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/yolov7-wind-turbine-detection } },
  url = { https://datasetninja.com/yolov7-wind-turbine-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Dataset Wind Turbine Detection (by Noah Vriese) 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 Noah Vriese)', 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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