The increasing accessibility and precision of Earth observation satellite data offers
considerable opportunities for industrial and state actors alike. This calls however for …

tslearn is a general-purpose Python machine learning library for time series that offers tools
for pre-processing and feature extraction as well as dedicated models for clustering …

Global change has tremendous impacts on savanna systems around the world. Processes
related to climate change or agricultural expansion threaten the ecosystem's state, function …

Given strong year-to-year variability, increasing competition for natural resources, and
climate change impacts on agriculture, monitoring global crop and natural vegetation …

Until recently, the vast majority of data mining time series classification (TSC) research has
focused on alternative distance measures for 1-Nearest Neighbour (1-NN) classifiers based …

TimeStats is a free tool for the analysis of multitemporal equidistant georeferenced remote
sensing data archives, such as MODIS, AVHRR, MERIS and SPOT-Vegetation. Key features …

Recently, two ideas have been explored that lead to more accurate algorithms for time-
series classification (TSC). First, it has been shown that the simplest way to gain …

New remote sensing sensors will acquire High spectral, spatial and temporal Resolution
Satellite Image Time Series (HR-SITS). These new data are of great interest to map land …

Large-scale, high-resolution maps of rapeseed (Brassica napus L.), a major oilseed crop,
are critical for predicting annual production and ensuring global energy security, but such …

Time series feature engineering is a time-consuming process because scientists and
engineers have to consider the multifarious algorithms of signal processing and time series …