Description du projet
Améliorer les estimations du volume de glace des glaciers
Les glaciers sont essentiels à la vie sur Terre. Cependant, en raison du réchauffement climatique, ils perdent de la masse, ce qui rend d’autant plus importante l’amélioration des estimations de leur volume de glace. Actuellement, ces estimations sont limitées par les difficultés à mesurer directement l’épaisseur de la glace. Financé par le programme Actions Marie Skłodowska-Curie, le projet SKYNET entend développer un modèle innovant basé sur l’apprentissage profond qui permettra d’exploiter l’énorme quantité de données satellitaires disponibles pour améliorer les estimations actuelles du volume de glace de l’ensemble des glaciers de la Terre. Le projet utilisera des architectures de pointe pour la reconstruction d’images, alimentées par des modèles d’élévation numérique par satellite, entre autres méthodes.
Objectif
Estimating the ice volume of Earth's glaciers is a grand challenge of Earth System science. Besides being a critical parameter to model glacier evolution, knowledge of glacier volume is fundamental to quantify global sea level rise and available freshwater resources. Under current global warming glaciers are losing mass, making improved glacier ice volume estimates a top-priority to constrain future climate scenarios. Direct glacier ice volume estimates are limited by difficulty in directly measuring the ice thickness. As a result, estimates rely on models, many of which depend on explicit physical laws but require parameters often poorly constrained. Today, the amount of satellite data is increasing at such a rate that it cannot be efficiently exploited by traditional processing pipelines. At the same time, Artificial Intelligence techniques are becoming increasingly dominant problem-solving techniques. In particular, deep learning models have recently shown the ability to surpass human accuracy in many scientific tasks. The goal of the SKYNET project is to develop an innovative deep learning-based model capable of exploiting the huge amount of available satellite data to improve the current estimates of ice volumes of all Earth’s glaciers, from continental alpine glaciers to polar glaciers, including those in the periphery of Greenland and Antarctica. The proposed methodology makes use of state-of-the art image inpainting architectures fed with satellite-based digital elevation models (TanDEM-X,REMA), altimetry (NASA’s ICESat-2), gravity and ice surface velocity data to infer subglacial topographies hence ice volumes. Modelled topographies will be constrained towards realistic solutions using glacier ice thickness measurements (GlaThiDa repository) from in-situ and remotely sensed observations. SKYNET will be jointly developed by two leading institutions in glaciology and remote sensing: the University of Venice and the University of California Irvine.
Champ scientifique
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringsatellite technology
- engineering and technologyenvironmental engineeringremote sensing
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesearth and related environmental sciencesgeophysics
- natural sciencesearth and related environmental sciencesphysical geographyglaciology
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régime de financement
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global FellowshipsCoordinateur
30123 Venezia
Italie