scholarly journals Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series

2015 ◽  
Vol 7 (5) ◽  
pp. 5098-5116 ◽  
Author(s):  
Tara Blakey ◽  
Assefa Melesse ◽  
Margaret Hall
Keyword(s):  
Time Series ◽  
Supervised Classification

scholarly journals BREIZHCROPS: A TIME SERIES DATASET FOR CROP TYPE MAPPING

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1545-1551
Author(s):  
M. Rußwurm ◽  
C. Pelletier ◽  
M. Zollner ◽  
S. Lefèvre ◽  
M. Körner
Keyword(s):  
Time Series ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Additional Data ◽  
Type Mapping ◽  
North East ◽  
Time Series Dataset ◽  
Crop Type ◽  
Sentinel 2

Abstract. We present BreizhCrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series. We aggregated label data and Sentinel-2 top-of-atmosphere as well as bottom-of-atmosphere time series in the region of Brittany (Breizh in local language), north-east France. We compare seven recently proposed deep neural networks along with a Random Forest baseline. The dataset, model (re-)implementations and pre-trained model weights are available at the associated GitHub repository (https://github.com/dl4sits/breizhcrops) that has been designed with applicability for practitioners in mind. We plan to maintain the repository with additional data and welcome contributions of novel methods to build a state-of-the-art benchmark on methods for crop type mapping.

scholarly journals Semi-supervised time series classification method for quantum computing

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sheir Yarkoni ◽  
Andrii Kleshchonok ◽  
Yury Dzerin ◽  
Florian Neukart ◽  
Marc Hilbert
Keyword(s):  
Time Series ◽  
Quantum Computing ◽  
Supervised Classification ◽  
Unsupervised Classification ◽  
Set Cover ◽  
Time Series Classification ◽  
Reconstruction Problem ◽  
Training Set ◽  
Set Cover Problem

AbstractIn this paper we develop methods to solve two problems related to time series (TS) analysis using quantum computing: reconstruction and classification. We formulate the task of reconstructing a given TS from a training set of data as an unconstrained binary optimization (QUBO) problem, which can be solved by both quantum annealers and gate-model quantum processors. We accomplish this by discretizing the TS and converting the reconstruction to a set cover problem, allowing us to perform a one-versus-all method of reconstruction. Using the solution to the reconstruction problem, we show how to extend this method to perform semi-supervised classification of TS data. We present results indicating our method is competitive with current semi- and unsupervised classification techniques, but using less data than classical techniques.

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