RarePlanes
2D Box
2D Polygon
Remote Sensing
|...
许可协议: 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

数据概要
数据格式
Image,
数据量
--
文件大小
316.03GB
发布方
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."
数据集反馈
| 297 | 数据量 -- | 大小 316.03GB
RarePlanes
2D Box 2D Polygon
Remote Sensing
许可协议: 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

数据集反馈
1
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