Descripción del proyecto
Aprendizaje profundo en la observación terrestre para obtener mejores datos
La observación terrestre (OT) está cambiando significativamente debido a la gran cantidad de observaciones obtenidas a partir de la teledetección y las redes de sensores «in situ» que adquieren mediciones localizadas muy precisas. A fin de calcular parámetros geofísicos se necesitan nuevas soluciones para obtener datos a partir de instrumentos terrestres y espaciales. Para comprender mejor los datos de múltiples fuentes de la OT, el proyecto CALCHAS, financiado con fondos europeos, obtendrá observaciones de diversas fuentes, combinará escalas de muestras asociadas con mediciones «in situ» y espaciales y analizará series temporales de observaciones dinámicas. Se usarán herramientas matemáticas para ampliar la actual capacidad de análisis de datos de una única fuente. El proyecto analizará series temporales de mediciones de microondas pasivas y activas, instrumentos de imagenología de espacios multiespectrales y mediciones de sensores «in situ».
Objetivo
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.
Ámbito científico
- 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
Programa(s)
Régimen de financiación
MSCA-IF-GF - Global FellowshipsCoordinador
70013 Irakleio
Grecia