Dataset Ninja LogoDataset Ninja:

Semantic Segmentation Satellite Imagery Dataset

261252
Tagaerial, safety, environmental, satellite
Tasksemantic segmentation
Release YearMade in 2022
LicenseCC BY 4.0
Download1 GB

Introduction #

The Semantic Segmentation Satellite Imagery dataset was taken from the project for the Kaggle Competition organised by CentraleSupelec Deep Learning course. The training dataset consisted of 261 images taken by a small UAV in the area of Houston, Texas to assess the damages after Hurricane Harvey. Each pixel was segmented into one of 25 classes such as property roof, trees / shrubs, road / highway, swimming pool, vehicle, flooded, etc.

ExpandExpand
Dataset LinkHomepageDataset LinkGitHub

Summary #

Semantic Segmentation with PyTorch Satellite Imagery is a dataset for a semantic segmentation task. It is used in the geospatial domain. Possible applications of the dataset could be in the search and rescue (SAR) and environmental industries.

The dataset consists of 261 images with 2478 labeled objects belonging to 25 different classes including background, grass, trees / shrubs, and other: property roof, vehicle, chimney, secondary structure, swimming pool, power lines & cables, window, road / highway, flooded, dense vegetation / forest, street light, water body, garbage bins, trampoline, satellite antenna, parking area - commercial, solar panels, under construction / in progress status, boat, sports complex / arena, industrial site, and water tank / oil tank.

Images in the Semantic Segmentation Satellite Imagery dataset have pixel-level semantic segmentation annotations. All images are labeled (i.e. with annotations). There is 1 split in the dataset: train (261 images). The dataset was released in 2022.

Here are the visualized examples for the classes:

Explore #

Semantic Segmentation Satellite Imagery dataset has 261 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 Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
OpenSample annotation mask from Semantic Segmentation Satellite ImagerySample image from Semantic Segmentation Satellite Imagery
πŸ‘€
Have a look at 261 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 25 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 1-10 of 25
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
backgroundβž”
mask
261
261
1
15.18%
trees / shrubsβž”
mask
254
254
1
16.24%
grassβž”
mask
254
254
1
29.21%
property roofβž”
mask
222
222
1
13.33%
vehicleβž”
mask
199
199
1
0.63%
chimneyβž”
mask
150
150
1
0.11%
secondary structureβž”
mask
144
144
1
0.49%
swimming poolβž”
mask
142
142
1
0.83%
power lines & cablesβž”
mask
137
137
1
1.8%
windowβž”
mask
120
120
1
0.07%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

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 1-10 of 25
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
background
mask
261
15.18%
64.03%
0.37%
3341px
72.76%
4592px
100%
4159px
99.79%
3000px
100%
3072px
100%
trees / shrubs
mask
254
16.24%
54.68%
0.14%
206px
5.15%
4592px
100%
3207px
76.53%
342px
11.13%
3072px
100%
grass
mask
254
29.21%
95.97%
0.3%
279px
6.97%
4592px
100%
3487px
83.87%
201px
6.7%
3072px
100%
property roof
mask
222
13.33%
35.84%
0.04%
54px
1.18%
4592px
100%
2380px
55.9%
104px
3.47%
3072px
100%
vehicle
mask
199
0.63%
7.84%
0%
1px
0.03%
4587px
99.95%
1724px
40.14%
41px
1.37%
3072px
100%
chimney
mask
150
0.11%
1.37%
0%
1px
0.03%
4497px
97.93%
1392px
32.16%
1px
0.03%
2997px
98.1%
secondary structure
mask
144
0.49%
3.92%
0.01%
46px
1.15%
4330px
99.75%
1185px
27.6%
35px
1.17%
3065px
99.87%
swimming pool
mask
142
0.83%
2.94%
0.08%
74px
1.85%
4488px
97.74%
1325px
30.54%
85px
2.83%
3069px
99.97%
power lines & cables
mask
137
1.8%
25.89%
0%
29px
0.63%
4592px
100%
1303px
31.06%
43px
1.4%
3072px
100%
window
mask
120
0.07%
0.3%
0.01%
23px
0.57%
4560px
99.3%
1120px
26.37%
28px
0.93%
3066px
99.8%

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 2478 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 2478
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”
background
mask
6625.jpg
4592 x 3072
4592px
100%
3072px
100%
33.07%
2βž”
grass
mask
6625.jpg
4592 x 3072
3983px
86.74%
3068px
99.87%
34.7%
3βž”
trees / shrubs
mask
6625.jpg
4592 x 3072
1730px
37.67%
3061px
99.64%
12.36%
4βž”
street light
mask
6625.jpg
4592 x 3072
156px
3.4%
50px
1.63%
0.03%
5βž”
garbage bins
mask
6625.jpg
4592 x 3072
45px
0.98%
49px
1.6%
0.01%
6βž”
road / highway
mask
6625.jpg
4592 x 3072
788px
17.16%
3064px
99.74%
7.96%
7βž”
flooded
mask
6625.jpg
4592 x 3072
1403px
30.55%
1430px
46.55%
11.87%
8βž”
background
mask
7239.jpg
4592 x 3072
4592px
100%
3072px
100%
18.03%
9βž”
property roof
mask
7239.jpg
4592 x 3072
3959px
86.22%
3072px
100%
22.44%
10βž”
swimming pool
mask
7239.jpg
4592 x 3072
1045px
22.76%
775px
25.23%
0.75%

License #

Semantic Segmentation with PyTorch Satellite Imagery is under CC BY 4.0 license.

Source

Citation #

If you make use of the Semantic-segmentation-Satellite-Imagery data, please cite the following reference:

@misc{alchimowicz_2022,
  title={semantic_segmentation_satellite_imagery}, 
  url={https://figshare.com/collections/semantic_segmentation_satellite_imagery/6026765/1}
  DOI={10.6084/m9.figshare.c.6026765.v1},
  abstractNote={<p>satellite imagery semantic segmentation</p>},
  publisher={figshare},
  author={Alchimowicz, Jedrzej},
  year={2022},
  month={Jun}
}

Source

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

@misc{ visualization-tools-for-semantic-segmentation-satellite-imagery-dataset,
  title = { Visualization Tools for Semantic Segmentation Satellite Imagery Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/semantic-segmentation-satellite-imagery } },
  url = { https://datasetninja.com/semantic-segmentation-satellite-imagery },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-21 },
}

Download #

Dataset Semantic Segmentation Satellite Imagery 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='Semantic Segmentation Satellite Imagery', 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:

. . .

Disclaimer #

Our gal from the legal dep told us we need to post this:

Dataset Ninja provides visualizations and statistics for some datasets that can be found online and can be downloaded by general audience. Dataset Ninja is not a dataset hosting platform and can only be used for informational purposes. The platform does not claim any rights for the original content, including images, videos, annotations and descriptions. Joint publishing is prohibited.

You take full responsibility when you use datasets presented at Dataset Ninja, as well as other information, including visualizations and statistics we provide. You are in charge of compliance with any dataset license and all other permissions. You are required to navigate datasets homepage and make sure that you can use it. In case of any questions, get in touch with us at hello@datasetninja.com.