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- Titel:
SSL4EO-S12: A Large-scale Multimodal Multitemporal Dataset for Self-supervised Learning in Earth Observation (8-bit)
- Dokumenttyp:
- Forschungsdaten
- Veröffentlichungsdatum:
- 28.03.2023
- Verantwortlich:
- Zhu, Xiaoxiang
- Autorinnen / Autoren:
- Wang, Yi; Ait Ali Braham, Nassim; Albrecht, Conrad M; Xiong, Zhitong; Liu, Chenying; Zhu, Xiaoxiang
- Institutionszugehörigkeit:
- TUM
- Herausgeber:
- TUM
- Enddatum der Datenerzeugung:
- 28.02.2023
- 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 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 covers an area of 2640mx2640m and includes four seasonal time stamps. The compressed dataset is provided in normalized 8-bit GeoTiff format, with each band being one single file. Details see https://github.com/zhu-xlab/SSL4EO-S12
- Links:
This is a compressed version (Sentinel-2 8-bit, Sentinel-1 8-bit) of the data. See download link under " Technical remarks"
The original version (Sentinel-2 16-bit, Sentinel-1 32-bit) can be found under: 10.14459/2022mp1660427.001- Schlagworte:
- Self-supervised Learning; Remote Sensing; Earth Observation; Sentinel-1/2
- Technische Hinweise:
- View and download (154 GB total, 5 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1702379):
rsync rsync://m1702379@dataserv.ub.tum.de/m1702379/ - Sprache:
- en
- Rechte:
- by, http://creativecommons.org/licenses/by/4.0
BibTeX