Descripción del proyecto
Mejora de las estimaciones del volumen de hielo de los glaciares
Los glaciares son fundamentales para la vida en la Tierra. Sin embargo, debido al calentamiento global, están perdiendo masa, lo que aumenta la importancia de mejorar las estimaciones de su volumen de hielo. Actualmente, estas estimaciones se ven limitadas por las dificultades para medir directamente el espesor del hielo. El proyecto SKYNET, financiado con fondos europeos, se propone desarrollar un modelo innovador basado en el aprendizaje profundo capaz de aprovechar la inmensa cantidad de datos satelitales disponibles para, de esto modo, mejorar las estimaciones actuales del volumen de hielo de todos los glaciares de la Tierra. Entre otros métodos, el equipo del proyecto utilizará programas de restauración de imagen de última generación alimentados con modelos digitales de elevación basados en satélites.
Objetivo
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.
Ámbito científico
- 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
Programa(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régimen de financiación
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global FellowshipsCoordinador
30123 Venezia
Italia