The modelling of Earth observation data is a challenging problem, typica...
In this paper, we study the applicability of active learning in operativ...
Defining an efficient training set is one of the most delicate phases fo...
Gaussian Processes (GPs) has experienced tremendous success in geoscienc...
In this work we evaluate multi-output (MO) Gaussian Process (GP) models ...
Developing accurate models of crop stress, phenology and productivity is...
Dealing with land cover classification of the new image sources has also...
Earth observation from satellite sensory data poses challenging problems...
Computationally expensive Radiative Transfer Models (RTMs) are widely us...
Kernel-based machine learning regression algorithms (MLRAs) are potentia...
The availability of satellite optical information is often hampered by t...
Most problems in Earth sciences aim to do inferences about the system, w...
New social and economic activities massively exploit big data and machin...
This paper presents a unified framework to tackle estimation problems in...