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RarePlanes
2D Box
Remote Sensing
|...
License: CC BY-SA 4.0

Overview

RarePlanes is a unique open-source machine learning dataset from CosmiQ Works and AI.Reverie
that incorporates both real and synthetically generated satellite imagery. The RarePlanes dataset
specifically focuses on the value of AI.Reverie synthetic data to aid computer vision algorithms
in their ability to automatically detect aircraft and their attributes in satellite imagery.

Data Collection

The real portion of the dataset consists of 253 Maxar WorldView-3 satellite images spanning
112 locations with ~14,700 hand annotated aircraft. The accompanying synthetic dataset is generated
via AI.Reverie’s simulation platform and features 50,000 synthetic satellite images with over
600,000 aircraft annotations. Both the real and synthetically generated aircraft feature 10
fine grain attributes including: aircraft length, wingspan, wing-shape, wing-position, FAA
wingspan class, propulsion, number of engines, number of vertical-stabilizers, if it has canards,
and aircraft role.

Data Annotation

Each aircraft is labeled in a diamond style with annotators instructed to label the nose, left-wing,
tail, and right-wing in order. This annotation style has the advantage of being simplistic,
easily reproducible, convertible to a bounding box, and ensures that aircraft are consistently
annotated as other formats can often lead to imprecise labeling. Furthermore, this annotation
style enables us to pull out two valuable features of aircraft: Their length and wingspan.

Data Format

Imagery: .tif, .png
Labels: .geojson, .json
Metadata: .xml, .csv, .txt

Citation

@misc{RarePlanes_Dataset,
title={RarePlanes Dataset},
author={Shermeyer, Jacob and Hossler, Thomas and Van Etten, Adam and Hogan, Daniel and Lewis,
Ryan and Kim, Daeil},    organization = {In-Q-Tel - CosmiQ Works and AI.Reverie},    month
= {June},
year = {2020} }
@article{RarePlanes_Paper,
title={RarePlanes: Synthetic Data Takes Flight},
author={Shermeyer, Jacob and Hossler, Thomas and Van Etten, Adam and Hogan,
Daniel and Lewis, Ryan and Kim, Daeil},    organization = {In-Q-Tel - CosmiQ Works and AI.Reverie},
   month = {June},
year = {2020} }

License

CC BY-SA 4.0

Data Summary
Type
Image,
Amount
--
Size
316.03GB
Provided by
COSMIQ WORKS
Founded in 2015 as a technology challenge lab within In-Q-Tel (IQT), CosmiQ Works is an IQT Lab focused on developing, prototyping, and evaluating emerging open source artificial intelligence capabilities for geospatial use cases. Artificial intelligence will fundamentally change how geospatial analytics is performed and CosmiQ Works helps accelerates development and adoption of these technologies into deployable products. And by the way, it’s pronounced "Cosmic."
| Amount -- | Size 316.03GB
RarePlanes
2D Box
Remote Sensing
License: CC BY-SA 4.0

Overview

RarePlanes is a unique open-source machine learning dataset from CosmiQ Works and AI.Reverie
that incorporates both real and synthetically generated satellite imagery. The RarePlanes dataset
specifically focuses on the value of AI.Reverie synthetic data to aid computer vision algorithms
in their ability to automatically detect aircraft and their attributes in satellite imagery.

Data Collection

The real portion of the dataset consists of 253 Maxar WorldView-3 satellite images spanning
112 locations with ~14,700 hand annotated aircraft. The accompanying synthetic dataset is generated
via AI.Reverie’s simulation platform and features 50,000 synthetic satellite images with over
600,000 aircraft annotations. Both the real and synthetically generated aircraft feature 10
fine grain attributes including: aircraft length, wingspan, wing-shape, wing-position, FAA
wingspan class, propulsion, number of engines, number of vertical-stabilizers, if it has canards,
and aircraft role.

Data Annotation

Each aircraft is labeled in a diamond style with annotators instructed to label the nose, left-wing,
tail, and right-wing in order. This annotation style has the advantage of being simplistic,
easily reproducible, convertible to a bounding box, and ensures that aircraft are consistently
annotated as other formats can often lead to imprecise labeling. Furthermore, this annotation
style enables us to pull out two valuable features of aircraft: Their length and wingspan.

Data Format

Imagery: .tif, .png
Labels: .geojson, .json
Metadata: .xml, .csv, .txt

Citation

@misc{RarePlanes_Dataset,
title={RarePlanes Dataset},
author={Shermeyer, Jacob and Hossler, Thomas and Van Etten, Adam and Hogan, Daniel and Lewis,
Ryan and Kim, Daeil},    organization = {In-Q-Tel - CosmiQ Works and AI.Reverie},    month
= {June},
year = {2020} }
@article{RarePlanes_Paper,
title={RarePlanes: Synthetic Data Takes Flight},
author={Shermeyer, Jacob and Hossler, Thomas and Van Etten, Adam and Hogan,
Daniel and Lewis, Ryan and Kim, Daeil},    organization = {In-Q-Tel - CosmiQ Works and AI.Reverie},
   month = {June},
year = {2020} }

License

CC BY-SA 4.0

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