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SEDAL focuses on developing innovative statistical learning methods for analyzing Earth Observation satellite data. The project aims to enhance the efficiency and accuracy of climate monitoring by creating models that can interpret complex and diverse datasets, ultimately contributing to a better understanding of envir…
SEDAL is an interdisciplinary project that aims to develop novel statistical learning methods to analyze Earth Observation (EO) satellite data.
In the last decade, machine 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.
In the coming few years, this problem will largely increase: several sat…
UNIVERSITAT DE VALENCIA
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