Description du projet
Un apprentissage profond de l’observation de la Terre pour de meilleures données
L’observation de la Terre (OT) est un domaine en pleine mutation du fait de la grande quantité d’observations obtenues grâce à la télédétection et aux réseaux de capteurs qui acquièrent des mesures localisées de haute précision. Il est nécessaire de disposer de nouvelles solutions afin d’obtenir des données issues d’instruments spatiaux et terrestres permettant d’estimer les paramètres géophysiques. Pour mieux comprendre les données d’OT multisources, le projet CALCHAS, financé par l’UE, recueillera des observations émanant de différentes sources, combinera les échelles d’échantillonnage associées aux mesures spatiale et et analysera les séries chronologiques d’observations dynamiques. Des outils mathématiques seront utilisés pour accroître la capacité actuelle d’analyse des données à source unique. Le projet analysera des séries chronologiques de mesures tirées d’instruments d’imagerie spatiaux hyperfréquences et multibandes actifs et passifs, ainsi que des mesures de capteurs .
Objectif
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.
Champ scientifique
- 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
Programme(s)
Régime de financement
MSCA-IF-GF - Global FellowshipsCoordinateur
70013 Irakleio
Grèce