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- Dokumenttyp:
- Forschungsdaten
- Veröffentlichungsdatum:
- 14.06.2022
- Verantwortlich:
- Zhu, Xiaoxiang
- Autorinnen / Autoren:
- Wang, Yi; Ait Ali Braham, Nassim; Albrecht, Conrad M; Xiong, Zhitong; Liu, Chenying; Zhu, Xiaoxiang
- Institutionszugehörigkeit:
- TUM
- Herausgeber:
- TUM
- Titel:
- SSL4EO-S12: A Large-scale Multimodal Multitemporal Dataset for Self-supervised Learning in Earth Observation
- Identifikator:
- doi:10.14459/2022mp1660427.001
- Konzept-DOI:
- doi:10.14459/2022mp1660427
- Enddatum der Datenerzeugung:
- 31.03.2022
- Fachgebiet:
- DAT Datenverarbeitung, Informatik; GEO Geowissenschaften
- Quellen der Daten:
- Abbildungen von Objekten / image of objects
- Datentyp:
- Bilder / images
- Methode der Datenerhebung:
- Automatic download, image preparation and processing using Google Earth Engine and Python
- Beschreibung:
- The SSL4EO-S12 dataset is a large-scale dataset for unsupervised/self-supervised pre-training in Earth observation. The dataset consists of unlabeled patch triplets (Sentinel-1 dual-pol SAR, Sentinel-2 top-of-atmosphere multispectral, Sentinel-2 surface reflectance multispectral) from 251079 locations across the globe, each patch covering 2640mx2640m and including four seasonal time stamps. The raw dataset is provided in GeoTiff format, with each band being one single file.
- Links:
Additional information: https://github.com/zhu-xlab/SSL4EO-S12
A compressed version (Sentinel-2 8-bit, Sentinel-1 8-bit) of the data can be found under: https://mediatum.ub.tum.de/1702379- Schlagworte:
- Self-supervised Learning; Remote Sensing; Earth Observation; Sentinel-1/2
- Technische Hinweise:
- View and download (1,5 TB total, 3 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1660427.001):
rsync rsync://m1660427.001@dataserv.ub.tum.de/m1660427.001/ - Sprache:
- en
- Rechte:
- by, http://creativecommons.org/licenses/by/4.0
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