Dataset List

Vol.|Area

No.11 Vol.11,2024
No.10 Vol.11,2024
No.09 Vol.11,2024
No.08 Vol.11,2024
No.07 Vol.11,2024
No.06 Vol.11,2024
No.05 Vol.11,2024
No.04 Vol.11,2024
No.03 Vol.11,2024
No.02 Vol.11,2024
No.01 Vol.11,2024
No.12 Vol.10,2023
No.11 Vol.10,2023
No.10 Vol.10,2023
No.09 Vol.10,2023
No.08 Vol.10,2023
No.07 Vol.10,2023
No.06 Vol.10,2023
No.05 Vol.10,2023
No.04 Vol.10,2023
No.03 Vol.10,2023
No.02 Vol.10,2023
No.01 Vol.10,2023
No.12 Vol.9,2022
No.11 Vol.9,2022
No.10 Vol.9,2022
No.09 Vol.9,2022
No.08 Vol.9,2022
No.07 Vol.9,2022
No.06 Vol.9,2022
No.05 Vol.9,2022
No.04 Vol.9,2022
No.03 Vol.9,2022
No.02 Vol.9,2022
No.01 Vol.9,2022
No.12 Vol.8,2021
No.11 Vol.8,2021
No.10 Vol.8,2021
No.09 Vol.8,2021
No.08 Vol.8,2021
No.07 Vol.8,2021
No.06 Vol.8,2021
No.05 Vol.8,2021
No.04 Vol.8,2021
No.03 Vol.8,2021
No.03 Vol.8,2021
No.02 Vol.8,2021
No.01 Vol.8,2021
No.09 Vol.7,2020
No.08 Vol.7,2020
No.07 Vol.7,2020
No.06 Vol.7,2020
No.05 Vol.7,2020
No.04 Vol.7,2020
No.03 Vol.7,2020
No.02 Vol.7,2020
No.01 Vol.7,2020
No.06 Vol.6,2019
No.05 Vol.6,2019
No.04 Vol.6,2019
No.03 Vol.6,2019
No.02 Vol.6,2019
No.01 Vol.6,2019
No.08 Vol.5,2018
No.07 Vol.5,2018
No.06 Vol.5,2018
No.05 Vol.5,2018
No.04 Vol.5,2018
No.03 Vol.5,2018
No.02 Vol.5,2018
No.01 Vol.5,2018
No.04 Vol.4,2017
No.03 Vol.4,2017
No.02 Vol.4,2017
No.01 Vol.4,2017
No.09 Vol.3,2016
No.08 Vol.3,2016
No.07 Vol.3,2016
No.06 Vol.3,2016
No.05 Vol.3,2016
No.04 Vol.3,2016
No.03 Vol.3,2016
No.02 Vol.3,2016
No.01 Vol.3,2016
No.02 Vol.2,2015
No.01 Vol.2,2015
No.02 Vol.1,2014
No.01 Vol.1,2014
China
Asia
Oceanic region
African
European
North American
South American
Arctic
Global Scale
Data Details

Dataset of Identifying Aircraft Groups by Remote Sensing Images


CHEN Junyu1,2LI Haiwei*1ZHANGGeng1WANG Shuang1CHEN Tieqiao1
1 Xi’an Institute of Optics and Precision Mechanics，Chinese Academy of Sciences，Xi’an 710119，China2 University of Chinese Academy of Sciences，Beijing 100049，China

DOI:10.3974/geodb.2020.03.25.V1

Published:Jun. 2020

Visitors：8404       Data Files Downloaded：305      
Data Downloaded：21140.00 MB      Citations：

Key Words：

Classification of remote sensing images,dataset of aircraft classification,Google Earth,Attention Mechanism

Abstract：

The aircraft is a typical object in remote sensing images. The authors selected several airports around the world and 3594 airplane images based on the public datasets - DIOR, UCAS_AOD, NWPU VHR-10, DOTA and Google earth images. Then seven aircraft groups in terms of wings and propellers based on the attention mechanism were identified. Following, the fourteen sub-groups in terms of color of aircraft and engine position were recognized. Dataset of Identifying Aircraft Groups by Remote Sensing Images include: (1) Swept-back wing aircraft; (2) Swept-back aircraft with leading edge; (3) Forward-swept wing airplane with trailing edge; (4) Delta-wing aircraft; (5) Flat-wing aircraft; (6) Propeller aircraft; (7) Helicopter. The dataset is archived in .png data format, and consists of 3594 data files with data size of 69.3MB (compressed to one single file with 69.3MB).Browse

Foundation Item：

Chinese Academy of Sciences (XAB2017B19)

Data Citation：

CHEN Junyu, LI Haiwei*, ZHANGGeng, WANG Shuang, CHEN Tieqiao. Dataset of Identifying Aircraft Groups by Remote Sensing Images[J/DB/OL]. Digital Journal of Global Change Data Repository, 2020. https://doi.org/10.3974/geodb.2020.03.25.V1.

CHEN Junyu, LI Haiwei, ZHANGGeng, et al. Dataset of aircraft classification by remote sensing images [J]. Journal of Global Change Data & Discovery, 2020, 4(2): 188-195. DOI: 10.3974/geodp.2020.02.12.

Data Product:

ID Data Name Data Size Operation
0Datapaper_OPT-Aircraft _v1.0.pdf356.00kbDownLoad
1 OPT-Aircraft _v1.0.rar 70974.94KB
Co-Sponsors
Superintend