Descrizione del progetto
Usare l’apprendimento profondo durante l’osservazione della Terra per ottenere dati migliori
L’osservazione della Terra sta cambiando notevolmente a causa delle numerosissime osservazioni ottenute tramite il telerilevamento e le reti di sensori in sito, in grado di acquisire misurazioni localizzate estremamente precise. Al fine di stimare i parametri geofisici sono dunque necessarie soluzioni innovative per ottenere dati dagli strumenti terrestri e a bordo di veicoli spaziali. Per comprendere meglio i dati multi fonte sull’osservazione della Terra, il progetto CALCHAS, finanziato dall’UE, raccoglierà osservazioni da fonti di vario tipo, combinerà scale di campionatura associate a misurazioni spaziali e in loco e analizzerà serie temporali di osservazioni dinamiche. I ricercatori impiegheranno inoltre strumenti matematici per estendere la capacità attuale dell’analisi di dati a fonte singola. Il progetto analizzerà misurazioni di sensori in sito e serie temporali di misurazioni effettuate da strumenti di immaginografia spaziali multispettro e a microonde attive e passive.
Obiettivo
Earth Observation (EO) is undergoing a radical transformation due to the massive volume of observations acquired by remote sensing and in-situ sensor networks. While satellites provide coarse-resolution, yet global-scale monitoring of environmental processes, in-situ sensor networks acquire high-accuracy localized measurements. Extracting information from spaceborne and ground based instruments requires innovative solutions which will allow the autonomous integration of diverse in nature and scale observations in order to provide high-quality geophysical parameter estimation. CALCHAS will demonstrate cutting edge technologies targeting three major factors towards the vision of fully automated multi-source EO data understanding, namely (i) the fusion of observations from different sources and modalities, (ii) the efficient aggregation of the sampling scales associated with spaceborne and in-situ measurements, and (iii) the analysis of time-series of dynamic observations. To that end, the paradigm-shifting signal processing and learning framework of Deep Learning will be utilized and extended through powerful mathematical tools and appropriate methodologies like supervised and generative learning, dramatically extending the current scope of single source data analysis. The developed framework will be employed for analyzing time-series of measurements from active and passive microwave and multispectral spaceborne imaging instruments (SMAP, SMOS and Sentinels), and in-situ sensor measurements, targeting the high-accuracy spatial and temporal resolution enhancement for observations and soil moisture estimation. The merits of the developed technology will be demonstrated in two intelligent water management case studies, namely optimized irrigation management and water pipeline leakage detection.
Campo scientifico
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorssmart sensors
- engineering and technologyenvironmental engineeringremote sensing
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- engineering and technologyenvironmental engineeringnatural resources managementwater management
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Programma(i)
Argomento(i)
Meccanismo di finanziamento
MSCA-IF-GF - Global FellowshipsCoordinatore
70013 Irakleio
Grecia