The tool is based on Deep Neural Networks (DNNs) and Synthetic Aperture Radar (SAR) backscatter data. Lorenzo Nava, Kushanav Bhuyan and Filippo Catani are among the contributors
23.04.2024
The tool is based on Deep Neural Networks (DNNs) and Synthetic Aperture Radar (SAR) backscatter data. Lorenzo Nava, Kushanav Bhuyan and Filippo Catani are among the contributors
IX Ciclo di Conferenze Itineranti - Distinguished Lectures SGI - SIMP | Tuesday 23 April from 4,15 pm to 6 pm | Arduino Classroom
23.04.2024
IX Ciclo di Conferenze Itineranti - Distinguished Lectures SGI - SIMP | Tuesday 23 April from 4,15 pm to 6 pm | Arduino Classroom
Speaker: Dott. Giovanni Baldo - Product Manager Fassa Bortolo S.r.l.I Tuesday 16 April - 4.30 PM | Arduino Classroom
16.04.2024
Industrial Minerals (IM) are essential to economic development: we can say they are the foundation of the economy. IM are usually cheap products, so it’s difficult to import them and they need to be mined/produced next to their main utilizers. This means we have the opportunity to apply the best
Speaker: Benjamin Mary - Institute of Agricultural Sciences - CSIC Spain I Tuesday 9 April - 4.30 PM | Arduino Classroom
09.04.2024
Water scarcity is a growing challenge in the Mediterranean region that is intensifying with global change as documented by FAO report. Accurate evapotranspiration (ET) estimation using remote sensing (RS) observations is vital for forest and crop irrigation management but faces challenges from cloud
Speaker: Warner Marzocchi, University of Naples, Federico II | Tuesday 26 March - 4.30 PM | Arduino Classroom
26.03.2024
Earthquake predictability is the essential scientific ingredient for any sound seismic risk mitigation planning. But what do we really know about earthquake predictability? Since the optimistic approach to deterministic earthquake prediction in the seventies, seismological community is moving towards probabilistic forecasts, in which seismologists can estimate probabilities for the earthquake occurrence in any specific space‐magnitude‐time windows.
