Early classification of time series has been extensively studied for minimizing class
prediction delay in time-sensitive applications such as medical diagnostic and industrial …

The rapid advancement of remote sensing technology has significantly enhanced the
temporal resolution of remote sensing data. Multitemporal remote sensing image …

Machine learning methods for satellite data have a range of societally relevant applications,
but labels used to train models can be difficult or impossible to acquire. Self-supervision is a …

Monitoring snow depth is important for applications such as hydrology, energy planning,
ecology, and safety evaluation for outdoor winter activities. Most methods able to estimate …

In the realm of conventional deep-learning-based pan-sharpening approaches, there has
been an ongoing struggle to harmonize the input panchromatic (PAN) and multi-spectral …

Over large areas, land cover classification has conventionally been undertaken using
satellite time series. Typically temporal metric percentiles derived from single pixel location …

Early time series classification predicts the class label of a given time series before it is
completely observed. In time-critical applications, such as arrhythmia monitoring in ICU …

The ever-increasing global population presents a looming threat to food production. To meet
growing food demands while minimizing negative impacts on water and soil, agricultural …

Early classification predicts the class of the incoming sequences before it is completely
observed. How to quickly classify streaming time series without losing interpretability …

For Landsat land cover classification, the time series observations are typically irregular in
the number of observations in a period (eg, a year) and acquisition dates due to cloud cover …