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Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
07.02.2022
Verantwortlich:
Zhu, Xiaoxiang
Autorinnen / Autoren:
Ebel, Patrick; Schmitt, Michael; Zhu, Xiaoxiang
Institutionszugehörigkeit:
TUM
Herausgeber:
TUM
Titel:
SEN12MS-CR-TS
Identifikator:
doi:10.14459/2022mp1639953
Enddatum der Datenerzeugung:
01.12.2021
Fachgebiet:
BAU Bauingenieurwesen, Vermessungswesen; DAT Datenverarbeitung, Informatik; GEO Geowissenschaften
Quellen der Daten:
Abbildungen von Objekten / image of objects
Datentyp:
Bilder / images
Methode der Datenerhebung:
Semi-automatic download and image preparation using Google Earth Engine, Python and GDAL, semi-automatic processing and clean-up
Beschreibung:
The SEN12MS-CR-TS dataset contains time series of corresponding Sentinel-1 dual-pol SAR data as well as cloudy and cloud-free Sentinel-2 multi-spectral images. The patches are distributed across the land masses of the Earth, providing a global sample distribution. The time points of the samples are evenly spaced throughout the entire year, covering all meteorological seasons. This is reflected by the dataset structure. All patches are pro-vided in the form of 16-bit GeoTiffs containing the follo...     »
Links:

This dataset relates to the publication: https://ieeexplore.ieee.org/document/9691348

Additional Information: https://patrickTUM.github.io/cloud_removal

Schlagworte:
Remote sensing, deep learning, data fusion, synthetic aperture radar imagery, optical imagery, cloud removal, time series data
Technische Hinweise:
View and download (498 GB total, 11 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1639953):
rsync rsync://m1639953@dataserv.ub.tum.de/m1639953/
Sprache:
en
Rechte:
by, http://creativecommons.org/licenses/by/4.0
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