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Title:

Temporal Vegetation Modelling using Long Short-Term Memory Networks for Crop Identification from Medium-Resolution Multi-Spectral Satellite Images

Document type:
Forschungsdaten
Publication date:
18.08.2017
Responsible:
Koch, Tobias
Authors:
Rußwurm, Marc; Körner, Marco
Author affiliation:
TUM: Chair of Remote Sensing Technology
Publisher:
TUM: Chair of Remote Sensing Technology
End date of data production:
01.06.2017
Subject area:
BAU Bauingenieurwesen, Vermessungswesen
Resource type:
Experimente und Beobachtungen / experiments and observations; Abbildungen von Objekten / image of objects
Other resource types:
Training- and evaluation data; RNN networks
Data type:
Bilder / images; mehrdimensionale Visualisierungen oder Modelle / models; Datenbanken / data bases
Other data type:
Serialized data containing multispectral Sentinel 2 images and TensorFlow checkpoints
Description:
Data supplements for the paper “Dataset for Temporal Vegetation Modelling using Long Short-Term Memory Networks for Crop Identification from Medium-Resolution Multi-Spectral Satellite Images” containing training- and evaluation data, evaluation methodology and neural network models.
Links:
This dataset is related to the publication https://mediatum.ub.tum.de/1369517
Key words:
Remote Sensing, EarthVision2017, Temporal Modelling, Recurrent Neural Networks, Field Identification
Technical remarks:
Follow github instructions ; https://www.github.com/TUM-LMF/fieldRNN ; https://www.lmf.bgu.tum.de/fieldRNN
View and download (5.4 GB total, 7 files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1370728):
rsync rsync://m1370728@dataserv.ub.tum.de/m1370728/
Language:
en
Rights:
by, http://creativecommons.org/licenses/by/4.0
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