The notion of uncertainty is of major importance in machine learning and constitutes a key
element of machine learning methodology. In line with the statistical tradition, uncertainty …

The unprecedented availability of optical satellite data in cloud-based computing platforms,
such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval …

The Gaussian process is an increasingly well-known type of stochastic process, which is a
generalization of the Gaussian probability distribution. It allows us to model complex …

Monitoring cropland phenology from optical satellite data remains a challenging task due to
the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to …

For the last decade, Gaussian process regression (GPR) proved to be a competitive
machine learning regression algorithm for Earth observation applications, with attractive …

Autonomous underwater vehicles (AUVs) are cost-and time-efficient systems for
environmental sampling. Informative adaptive sampling has been shown to be an effective …

The sensor-based structural health monitoring (SHM) systems are widely embedded in the
new-constructed and rehabilitated dam. Due to the harsh working environment, poor …

Traditionally, the pose and velocity prediction of a system at time t 2 given inertial
measurements from a 6-DoF IMU depends on the knowledge of the system's state at time t 1 …

Probably the most important technological invention of the previous century was the
transistor. And another very important invention was the digital computer, which got a …

Robots such as autonomous underwater vehicles (AUVs) and autonomous surface vehicles
(ASVs) have been used for sensing and monitoring aquatic environments such as oceans …