6 papers
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping
Benjamin Lucas, Charlotte Pelletier, Daniel Schmidt +2
Land cover maps are a vital input variable to many types of environmental research and management. While they can be produced automatically by machine learning techniques, these te…
InceptionTime: Finding AlexNet for Time Series Classification
Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier +7
This paper brings deep learning at the forefront of research into Time Series Classification (TSC). TSC is the area of machine learning tasked with the categorization (or labelling…
TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification
Ahmed Shifaz, Charlotte Pelletier, Francois Petitjean +1
Time Series Classification (TSC) has seen enormous progress over the last two decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles) is the current sta…
BreizhCrops: A Time Series Dataset for Crop Type Mapping
Marc Rußwurm, Charlotte Pelletier, Maximilian Zollner +2
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-atmosp…
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
Charlotte Pelletier, Geoffrey I. Webb, Francois Petitjean
New remote sensing sensors now acquire high spatial and spectral Satellite Image Time Series (SITS) of the world. These series of images are a key component of classification syste…
Proximity Forest: An effective and scalable distance-based classifier for time series
Benjamin Lucas, Ahmed Shifaz, Charlotte Pelletier +5
Research into the classification of time series has made enormous progress in the last decade. The UCR time series archive has played a significant role in challenging and guiding…