Dataset Ninja LogoDataset Ninja:

Power Plant Satellite Imagery Dataset

8908121
Tagaerial, energy-and-utilities
Taskinstance segmentation
Release YearMade in 2017
LicenseCC BY 4.0
Download21 GB

Introduction #

Released 2017-08-16 Β·Kyle Bradbury, Benjamin Brigman, Gouttham Chandrasekaret al.

The authors present the Power Plant Satellite Imagery Dataset (US Power Plants NAIP/LANDSAT8), comprising 4,454 instances of satellite imagery capturing mainland power plants in the United States. The dataset offers both high resolution (1m) and medium resolution (30m data pansharpened to 15m) for detection and segmentation tasks. The data sources include NAIP for high-resolution imagery, Landsat8 for medium-resolution imagery, and EPA EGRID documents for latitude and longitude locations

image

Example of NAIP/LANDSAT8 images and corresponding masks.

The dataset was created by the authors, who are part of the Duke University Electricity Access Team within the Duke Data+ Program. The primary objective is to detect electricity access in developing countries using aerial imagery. Power plants were specifically chosen as indicators due to their clear association with electricity access and their visibility in globally-available 30m resolution satellite imagery.

To ensure annotation accuracy, the authors employed a two-fold approach. Firstly, they conducted a visual inspection of all submissions, rejecting annotations that were blatantly incorrect. Secondly, multiple annotators reviewed each image, aiming to mitigate the impact of human error. The confidence in each pixel was established by considering the number of annotators including it in their annotations. Subsequently, a threshold was applied to create final binary labels.

The dataset offers various possibilities for data usage. The authors have previously explored image segmentation through pixel-based classification. It can also be leveraged for multi-class training, allowing users to classify by energy type. For those interested in deep learning problems, the dataset is particularly suitable for implementing Fully Convolutional Networks (FCN). Additionally, researchers in economics, statistics, energy, and the environment may find valuable insights in the energy type, capacity, and emission data provided by the dataset.

Data description

NAIP Imagery (uspp_naip)

  • High-resolution power plant images (1m/pixel, ~1115x1115 pix, ~5M/ea).
  • Utilized for ground truth gathering (outline of power plants) with human effort.
  • Acts as a reference for resolving confusions in lower resolutions.

Landsat8 Imagery (uspp_landsat)

  • Medium-resolution power plant images (15m/pixel, ~75x75 pix, ~17K/ea).
  • Intended for machine learning practice.

Fuel types:

Fuel Category Description (the primary fuel is derived from)
GAS gas
SOLAR solar power
HYDRO hydro power
WIND wind power
BIOMASS biomass sources
COAL coal
OIL oil
GEOTHERMAL geothermal power
OTHF waste heat/unknown/purchased
NUCLEAR nuclear
UNKNOWN not on the document
OFSL another fossil fuel
Total -
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Dataset LinkHomepageDataset LinkDataset Overview

Summary #

Power Plant Satellite Imagery Dataset (US Power Plants NAIP/LANDSAT8) is a dataset for instance segmentation, semantic segmentation, object detection, and classification tasks. It is used in the energy industry.

The dataset consists of 8908 images with 5185 labeled objects belonging to 1 single class (power plant).

Images in the Power Plant Satellite Imagery Dataset dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. There are 4454 (50% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Alternatively, the dataset could be split into 12 fuel types: gas (2320 images), solar (1700 images), hydro (1376 images), wind (914 images), biomass (854 images), coal (714 images), oil (652 images), geothermal (118 images), otfh (106 images), nuclear (92 images), unknown (38 images), and ofsl (24 images). Additionally, every image has information about its source, eGrid_id, state, and category. The dataset was released in 2017 by the Duke University, USA.

Here are the visualized examples for the classes:

Explore #

Power Plant Satellite Imagery Dataset dataset has 8908 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 Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
OpenSample annotation mask from Power Plant Satellite Imagery DatasetSample image from Power Plant Satellite Imagery Dataset
πŸ‘€
Have a look at 8908 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
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
power plantβž”
mask
4454
5185
1.16
20.19%

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
power plant
mask
5185
17.34%
98.44%
0%
5px
0.45%
1113px
99.82%
442px
39.64%
4px
0.36%
1113px
99.82%

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 5185 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 5185
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
power plant
mask
naip_1231_CA_OBG.tif.tiff
1114 x 1115
656px
58.89%
368px
33%
16.51%
2βž”
power plant
mask
naip_2029_DE_SUN.tif.tiff
1114 x 1114
1030px
92.46%
757px
67.95%
42.16%
3βž”
power plant
mask
naip_3437_KY_WAT.tif.tiff
1114 x 1114
206px
18.49%
457px
41.02%
2.19%
4βž”
power plant
mask
naip_1267_CA_NG.tif.tiff
1114 x 1114
277px
24.87%
301px
27.02%
6.23%
5βž”
power plant
mask
naip_7951_VA_WDS.tif.tiff
1114 x 1115
340px
30.52%
540px
48.43%
9%
6βž”
power plant
mask
naip_7951_VA_WDS.tif.tiff
1114 x 1115
360px
32.32%
326px
29.24%
6.51%
7βž”
power plant
mask
naip_4706_MS_NG.tif.tiff
1115 x 1114
195px
17.49%
230px
20.65%
2.85%
8βž”
power plant
mask
naip_4736_MT_WND.tif.tiff
1114 x 1114
290px
26.03%
476px
42.73%
5.06%
9βž”
power plant
mask
naip_6917_PA_NG.tif.tiff
1114 x 1114
1077px
96.68%
1070px
96.05%
63.14%
10βž”
power plant
mask
naip_5653_NJ_SUN.tif.tiff
1115 x 1114
596px
53.45%
635px
57%
16.89%

License #

Power Plant Satellite Imagery Dataset (US Power Plants NAIP/LANDSAT8) is under CC BY 4.0 license.

Source

Citation #

If you make use of the Power Plant Satellite Imagery Dataset data, please cite the following reference:

@article{Bradbury2017,
  author = "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 = "Power Plant Satellite Imagery Dataset",
  year = "2017",
  month = "8",
  url = "https://figshare.com/articles/dataset/Power_Plant_Satellite_Imagery_Dataset/5307364",
  doi = "10.6084/m9.figshare.5307364.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-power-plant-satellite-imagery-dataset-dataset,
  title = { Visualization Tools for Power Plant Satellite Imagery Dataset Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/power-plant-satellite-imagery-dataset } },
  url = { https://datasetninja.com/power-plant-satellite-imagery-dataset },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset Power Plant Satellite Imagery Dataset 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='Power Plant Satellite Imagery Dataset', 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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