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- Title:
Self-attention for raw optical Satellite Time Series Classification
- Document type:
- Forschungsdaten
- Publication date:
- 01.07.2021
- Responsible:
- Körner, Marco
- Authors:
- Rußwurm, Marc ; Körner, Marco
- Author affiliation:
- TUM
- Publisher:
- TUM
- End date of data 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
- 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- 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
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