SEDAL: Statistical Learning for Earth Observation Data Analysis

SEDAL is a research project funded by the European Research Council (ERC) Consolidator Grant 2015-2020, and directed by Prof. Gustau Camps-Valls at the Universitat de València, Spain. SEDAL is an interdisciplinary project that aims to develop novel statistical learning methods to analyze Earth Observation (EO) satellite data. In the last decade, learning models have helped to monitor land, oceans, and atmosphere through the analysis and estimation of climate and biophysical parameters. Current approaches, however, cannot deal efficiently with the particular characteristics of remote sensing data. This problem increases with the operational EU Copernicus Sentinel services, and we face now the urgent need to process and understand huge amounts of complex, heterogeneous, multisource, and structured data to monitor the rapid changes already occurring in our Planet. SEDAL aims to develop the next generation of statistical inference methods for EO data analysis. We develop advanced regression methods to improve efficiency, prediction accuracy and uncertainties, encode physical knowledge about the problem, and attain self-explanatory models learned from empirical data. Even more importantly, we learn graphical causal models to explain the potentially complex interactions between key observed variables, and discover hidden essential drivers and confounding factors. This project tackels the fundamental problem of moving from correlation to dependence and then to causation through EO data analysis. The theoretical developments are guided by the challenging problems of estimating biophysical parameters and learning causal relations at both local and global planetary scales. Check out the details in the proposal, reporting and outreach section

Motivation

Main objectives

Project structure