SSL4EO-S12: A large-scale multimodal, multitemporal dataset for self-supervised learning in Earth observation [Software and Data Sets]

Wang, Yi and Ait Ali Braham, Nassim and Xiong, Zhitong and Liu, Chenying and Albrecht, Conrad M. and Zhu, Xiao Xiang (2023) SSL4EO-S12: A large-scale multimodal, multitemporal dataset for self-supervised learning in Earth observation [Software and Data Sets]. IEEE Geoscience and Remote Sensing Magazine (GRSM), 11 (3), pp. 98-106. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/MGRS.2023.3281651. ISSN 2168-6831.

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Official URL: https://dx.doi.org/10.1109/MGRS.2023.3281651

Item URL in elib:

https://elib.dlr.de/199502/

Document Type:

Article

Title:

SSL4EO-S12: A large-scale multimodal, multitemporal dataset for self-supervised learning in Earth observation [Software and Data Sets]

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Wang, Yi	UNSPECIFIED	https://orcid.org/0000-0002-3096-6610	UNSPECIFIED
Ait Ali Braham, Nassim	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Xiong, Zhitong	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Liu, Chenying	UNSPECIFIED	https://orcid.org/0000-0001-9172-3586	147176325
Albrecht, Conrad M.	UNSPECIFIED	https://orcid.org/0009-0009-2422-7289	UNSPECIFIED
Zhu, Xiao Xiang	UNSPECIFIED	https://orcid.org/0000-0001-5530-3613	UNSPECIFIED

Date:

25 September 2023

Journal or Publication Title:

IEEE Geoscience and Remote Sensing Magazine (GRSM)

Refereed publication:

Yes

Open Access:

Yes

Gold Open Access:

In SCOPUS:

Yes

In ISI Web of Science:

Yes

Volume:

DOI:

10.1109/MGRS.2023.3281651

Page Range:

pp. 98-106

Publisher:

IEEE - Institute of Electrical and Electronics Engineers

ISSN:

2168-6831

Status:

Published

Keywords:

remote sensing, self-supervised learning, foundation model, dataset

HGF - Research field:

Aeronautics, Space and Transport

HGF - Program:

Space

HGF - Program Themes:

Earth Observation

DLR - Research area:

Raumfahrt

DLR - Program:

R EO - Earth Observation

DLR - Research theme (Project):

R - Artificial Intelligence

Location:

Oberpfaffenhofen

Institutes and Institutions:

Remote Sensing Technology Institute > EO Data Science

Deposited By:

Wang, Yi

Deposited On:

22 Nov 2023 13:30

Last Modified:

27 Feb 2024 18:14

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