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Titel:

Self-attention for raw optical Satellite Time Series Classification

Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
01.07.2021
Verantwortlich:
Körner, Marco
Autorinnen / Autoren:
Rußwurm, Marc ; Körner, Marco
Institutionszugehörigkeit:
TUM
Herausgeber:
TUM
Enddatum der Datenerzeugung:
07.04.2020
Fachgebiet:
BAU Bauingenieurwesen, Vermessungswesen; DAT Datenverarbeitung, Informatik; GEO Geowissenschaften
Quellen der Daten:
Abbildungen von Objekten / image of objects
Andere Quellen der Daten:
Zipped Image and Time Series Data
Datentyp:
Bilder / images; mehrdimensionale Visualisierungen oder Modelle / models; Tabellen / tables
Methode der Datenerhebung:
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
Beschreibung:
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.
Links:

Link Artikel: https://www.sciencedirect.com/science/article/abs/pii/S0924271620301647 and https://mediatum.ub.tum.de/1602502
DOI: 10.1016/j.isprsjprs.2020.06.006
Code Repository: https://github.com/MarcCoru/crop-type-mapping

Schlagworte:
Satellite Time Series analysis; Multi-temporal image analysis; Vegetation classification; Crop Type Mapping; Deep Learning; Machine Learning
Technische Hinweise:
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/
Sprache:
en
Rechte:
by, http://creativecommons.org/licenses/by/4.0
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