Airbus Aircraft Detection

Introduction #

Jeff Faudi

The Airbus Aircrafts Detection Sample Dataset is a subset derived from larger deep learning datasets developed using Airbus satellite imagery. The authors of the dataset present this version for illustrative purposes. The dataset aims to showcase the capabilities of Deep Learning in automatically detecting aircraft attributes such as their number, size, and type on airport sites. This automated detection provides insights into airport activities.

The primary data source for the dataset is Airbus’ Pleiades twin satellites, which consistently capture images of airports around the world. The dataset contains 103 image extracts from Pleiades imagery, each at approximately 50 cm resolution. These images are saved as JPEG files with dimensions of 2560 x 2560 pixels, equating to an area of 1280 meters on the ground. The dataset encompasses various airports globally, with some appearing multiple times on different acquisition dates. To add diversity, certain images feature fog or cloud cover.

The dataset has been meticulously annotated with bounding boxes outlining all aircraft in the provided images. These annotations are structured as closed GeoJSON polygons.

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Homepage

Summary #

Airbus Aircrafts Detection Sample Dataset is a dataset for an object detection task. It is used in the aviation industry.

The dataset consists of 109 images with 3425 labeled objects belonging to 2 different classes including airplane and truncated_airplane.

Images in the Airbus Aircraft Detection dataset have bounding box annotations. There are 6 (6% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: images (103 images) and extras (6 images). The dataset was released in 2021 by the Airbus DS Intelligence.

Explore #

Airbus Aircraft Detection dataset has 109 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 Airbus Aircraft Detection

Sample image from Airbus Aircraft Detection

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Have a look at 109 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.

Rows 1-2 of 2

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
Airplane➔ Unknown	103	3316	32.19	5.4%
Truncated_airplane➔ Unknown	54	109	2.02	0.23%

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 1-2 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
Airplane Unknown	3316	0.18%	0.78%	0.01%	30px	1.17%	231px	9.02%	101px	3.94%	29px	1.13%	230px	8.98%
Truncated_airplane Unknown	109	0.12%	0.52%	0.02%	27px	1.05%	180px	7.03%	90px	3.51%	23px	0.9%	201px	7.85%

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 3425 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 3425

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➔	Airplane Unknown	b7ab0316-bf02-4266-b44a-cef6417f795c.jpg	2560 x 2560	77px	3.01%	80px	3.12%	0.09%
2➔	Airplane Unknown	b7ab0316-bf02-4266-b44a-cef6417f795c.jpg	2560 x 2560	123px	4.8%	133px	5.2%	0.25%
3➔	Airplane Unknown	b7ab0316-bf02-4266-b44a-cef6417f795c.jpg	2560 x 2560	116px	4.53%	125px	4.88%	0.22%
4➔	Airplane Unknown	b7ab0316-bf02-4266-b44a-cef6417f795c.jpg	2560 x 2560	106px	4.14%	131px	5.12%	0.21%
5➔	Airplane Unknown	b7ab0316-bf02-4266-b44a-cef6417f795c.jpg	2560 x 2560	76px	2.97%	87px	3.4%	0.1%
6➔	Airplane Unknown	b7ab0316-bf02-4266-b44a-cef6417f795c.jpg	2560 x 2560	132px	5.16%	127px	4.96%	0.25%
7➔	Airplane Unknown	b7ab0316-bf02-4266-b44a-cef6417f795c.jpg	2560 x 2560	77px	3.01%	83px	3.24%	0.1%
8➔	Truncated_airplane Unknown	b7ab0316-bf02-4266-b44a-cef6417f795c.jpg	2560 x 2560	76px	2.97%	101px	3.95%	0.11%
9➔	Airplane Unknown	b7ab0316-bf02-4266-b44a-cef6417f795c.jpg	2560 x 2560	93px	3.63%	84px	3.28%	0.12%
10➔	Truncated_airplane Unknown	4fdabd34-a2fd-4f0a-bb48-01fe043f1499.jpg	2560 x 2560	82px	3.2%	34px	1.33%	0.04%

License #

Airbus Aircrafts Detection Sample Dataset is under CC BY-NC 4.0 license.

Source

Citation #

If you make use of the Airbus Aircraft Detection data, please cite the following reference:

@dataset{Airbus Aircraft Detection,
	author={Jeff Faudi},
	title={Airbus Aircrafts Detection Sample Dataset},
	year={2021},
	url={https://www.kaggle.com/datasets/airbusgeo/airbus-aircrafts-sample-dataset?select=README.md}
}

Source

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

@misc{ visualization-tools-for-airbus-aircraft-detection-dataset,
  title = { Visualization Tools for Airbus Aircraft Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/airbus-aircraft-detection } },
  url = { https://datasetninja.com/airbus-aircraft-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Dataset Airbus Aircraft Detection 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='Airbus Aircraft Detection', 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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