Semantic Segmentation Satellite Imagery

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.

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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.

Sample annotation mask from Semantic Segmentation Satellite Imagery

Sample image from Semantic Segmentation Satellite Imagery

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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.

Rows 1-10 of 25

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
background➔ mask	261	261	1	15.18%
trees / shrubs➔ mask	254	254	1	16.24%
grass➔ mask	254	254	1	29.21%
property roof➔ mask	222	222	1	13.33%
vehicle➔ mask	199	199	1	0.63%
chimney➔ mask	150	150	1	0.11%
secondary structure➔ mask	144	144	1	0.49%
swimming pool➔ mask	142	142	1	0.83%
power lines & cables➔ mask	137	137	1	1.8%
window➔ 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.

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.

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.

Rows 1-10 of 2478

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➔	background mask	6625.jpg	4592 x 3072	4592px	100%	3072px	100%	33.07%
2➔	grass mask	6625.jpg	4592 x 3072	3983px	86.74%	3068px	99.87%	34.7%
3➔	trees / shrubs mask	6625.jpg	4592 x 3072	1730px	37.67%	3061px	99.64%	12.36%
4➔	street light mask	6625.jpg	4592 x 3072	156px	3.4%	50px	1.63%	0.03%
5➔	garbage bins mask	6625.jpg	4592 x 3072	45px	0.98%	49px	1.6%	0.01%
6➔	road / highway mask	6625.jpg	4592 x 3072	788px	17.16%	3064px	99.74%	7.96%
7➔	flooded mask	6625.jpg	4592 x 3072	1403px	30.55%	1430px	46.55%	11.87%
8➔	background mask	7239.jpg	4592 x 3072	4592px	100%	3072px	100%	18.03%
9➔	property roof mask	7239.jpg	4592 x 3072	3959px	86.22%	3072px	100%	22.44%
10➔	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 = { may },
  note = { visited on 2024-05-20 },
}

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:

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