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Document type:
Forschungsdaten 
Publication date:
01.07.2021 
Authors:
Rußwurm, Marc ; Körner, Marco 
Author affiliation:
TUM 
Publisher:
TUM 
Title:
Self-attention for raw optical Satellite Time Series Classification 
Time of production:
07.04.2020 
Subject area:
BAU Bauingenieurwesen, Vermessungswesen; DAT Datenverarbeitung, Informatik; GEO Geowissenschaften 
Resource type:
Abbildungen von Objekten / image of objects 
Other resource types:
Zipped Image and Time Series Data 
Data type:
Bilder / images; mehrdimensionale Visualisierungen oder Modelle / models; Tabellen / tables 
Description:
Dataset for crop type mapping in Bavaria. Contains raw (unpreprocessed) Sentinel 2 satellite time series acquired over the year 2018 associated with crop type labels of the field parcels. This dataset was used to compare several deep learning models on preprocessed satellite data to industry standards with raw satellite time series to assess the robustness of the tested deep learning models to noise in the data. 
Method of data assessment:
Download from Sentinel 2 satellite data archive, image processing (cropping to relevant field parcels). Association with crop type labels acquired from the Bavarian Ministry of Agriculture 
Key words:
Satellite Time Series analysis; Multi-temporal image analysis; Vegetation classification; Crop Type Mapping; Deep Learning; Machine Learning 
Technical remarks:
View and download (2.66 GB, 6 files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1612845):
rsync rsync://m1612845@dataserv.ub.tum.de/m1612845/ 
Language:
en 
Rights:
by, http://creativecommons.org/licenses/by/4.0