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CitySegmentation Dataset

5022136
Tagaerial, satellite
Taskinstance segmentation
Release YearMade in 2019
LicenseODbL v1.0
Download2 GB

Summary #

Dataset LinkHomepage

CitySegmentation is a dataset for instance segmentation, semantic segmentation, and object detection tasks. Possible applications of the dataset could be in the geospatial domain.

The dataset consists of 50 images with 9306 labeled objects belonging to 2 different classes including building and road.

Images in the CitySegmentation 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. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. Additionally, images have city tag: Berlin or London. The dataset was released in 2019.

Dataset Poster

Explore #

CitySegmentation dataset has 50 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 CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
OpenSample annotation mask from CitySegmentationSample image from CitySegmentation
πŸ‘€
Have a look at 50 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 2 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 1-2 of 2
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
roadβž”
mask
50
955
19.1
9.83%
buildingβž”
mask
50
8351
167.02
31.78%

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.

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 1-2 of 2
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
building
mask
8351
0.19%
7.06%
0%
2px
0.06%
2871px
59.81%
164px
4.36%
3px
0.06%
3994px
79.5%
road
mask
955
0.52%
14.05%
0%
2px
0.06%
4800px
100%
379px
9.27%
9px
0.18%
5024px
100%

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 9306 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 9306
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”
building
mask
london_4_1_raster.png.tiff
3216 x 4288
107px
3.33%
245px
5.71%
0.05%
2βž”
building
mask
london_4_1_raster.png.tiff
3216 x 4288
51px
1.59%
175px
4.08%
0.03%
3βž”
building
mask
london_4_1_raster.png.tiff
3216 x 4288
114px
3.54%
353px
8.23%
0.11%
4βž”
building
mask
london_4_1_raster.png.tiff
3216 x 4288
29px
0.9%
132px
3.08%
0.02%
5βž”
building
mask
london_4_1_raster.png.tiff
3216 x 4288
377px
11.72%
198px
4.62%
0.13%
6βž”
building
mask
london_4_1_raster.png.tiff
3216 x 4288
351px
10.91%
638px
14.88%
0.11%
7βž”
building
mask
london_4_1_raster.png.tiff
3216 x 4288
51px
1.59%
212px
4.94%
0.04%
8βž”
building
mask
london_4_1_raster.png.tiff
3216 x 4288
127px
3.95%
340px
7.93%
0.21%
9βž”
building
mask
london_4_1_raster.png.tiff
3216 x 4288
454px
14.12%
903px
21.06%
0.43%
10βž”
building
mask
london_4_1_raster.png.tiff
3216 x 4288
47px
1.46%
238px
5.55%
0.03%

License #

CitySegmentation is under ODbL v1.0 license.

Source

Citation #

If you make use of the CitySegmentation data, please cite the following reference:

@dataset{CitySegmentation,
  author={},
  title={CitySegmentation},
  year={2019},
  url={https://www.kaggle.com/datasets/cceekkigg/berlin-aoi-dataset}
}

Source

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

@misc{ visualization-tools-for-city-segmentation-dataset,
  title = { Visualization Tools for CitySegmentation Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/city-segmentation } },
  url = { https://datasetninja.com/city-segmentation },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Dataset CitySegmentation 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='CitySegmentation', 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 #

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