New social and economic activities massively exploit big data and machine learning algorithms
to do inference on people’s lives. Applications include automatic curricula evaluation, …

Developing accurate models of crop stress, phenology and productivity is of paramount
importance, given the increasing need of food. Earth observation (EO) remote sensing data …

This chapter provides an overview of the different Artificial Intelligence (AI) systems that are
being used in contemporary digital tools for Mathematics Education (ME). It is aimed at …

The continuous monitoring of the terrestrial Earth system by a growing number of optical
satellite missions provides valuable insights into vegetation and cropland characteristics. …

In preparation for new-generation imaging spectrometer missions and the accompanying
unprecedented inflow of hyperspectral data, optimized models are needed to generate …

Establishing causal relations between random variables from observational data is perhaps
the most important challenge in today's science. In remote sensing and geosciences, this is …

Current remote sensing image classification problems have to deal with an unprecedented
amount of heterogeneous and complex data sources. Upcoming missions will soon provide …

Kernel methods are powerful machine learning techniques which use generic non-linear
functions to solve complex tasks. They have a solid mathematical foundation and exhibit …

Computational thinking (CT) is increasingly incorporated into curricular planning across
various educational levels in numerous countries. Presently, CT is being integrated into …

Food security is at stake, with climate change heavily impacting agriculture and food production.
In the present context of extreme events and changing conditions, developing advanced …