Niccolò Maffezzoli
@nicomaffe.bsky.social
Machine Learning | Earth Science
UC Irvine, University of Venice
UC Irvine, University of Venice
Alaska Bering-Malaspina-Seward glacier basins.
Ice thickness modeled with machine learning. Malaspina, Agassiz, Steller and Bering termini are grounded 100-300 meters below sea level. Hubbard glacier terminus is grounded up to half a kilometer below sea level in the Disenchantment Bay.
Ice thickness modeled with machine learning. Malaspina, Agassiz, Steller and Bering termini are grounded 100-300 meters below sea level. Hubbard glacier terminus is grounded up to half a kilometer below sea level in the Disenchantment Bay.
November 25, 2025 at 8:50 AM
Alaska Bering-Malaspina-Seward glacier basins.
Ice thickness modeled with machine learning. Malaspina, Agassiz, Steller and Bering termini are grounded 100-300 meters below sea level. Hubbard glacier terminus is grounded up to half a kilometer below sea level in the Disenchantment Bay.
Ice thickness modeled with machine learning. Malaspina, Agassiz, Steller and Bering termini are grounded 100-300 meters below sea level. Hubbard glacier terminus is grounded up to half a kilometer below sea level in the Disenchantment Bay.
The Geikie Plateau is a huge mass of ice connected to the Greenland ice sheet and the Atlantic Ocean.
How much ice is stored here ?
A machine learning model reveals some interesting ice veins.
How much ice is stored here ?
A machine learning model reveals some interesting ice veins.
May 17, 2025 at 4:56 PM
The Geikie Plateau is a huge mass of ice connected to the Greenland ice sheet and the Atlantic Ocean.
How much ice is stored here ?
A machine learning model reveals some interesting ice veins.
How much ice is stored here ?
A machine learning model reveals some interesting ice veins.
Glacier ice thickness maps produced globally with a machine learning system.
@ucirvine.bsky.social @unive Ca' Foscari
gmd.copernicus.org/articles/18/...
@ucirvine.bsky.social @unive Ca' Foscari
gmd.copernicus.org/articles/18/...
A gradient-boosted tree framework to model the ice thickness of the world's glaciers (IceBoost v1.1)
Abstract. Knowledge of glacier ice volumes is crucial for constraining future sea level potential, evaluating freshwater resources, and assessing impacts on societies, from regional to global. Motivat...
gmd.copernicus.org
May 9, 2025 at 5:51 AM
Glacier ice thickness maps produced globally with a machine learning system.
@ucirvine.bsky.social @unive Ca' Foscari
gmd.copernicus.org/articles/18/...
@ucirvine.bsky.social @unive Ca' Foscari
gmd.copernicus.org/articles/18/...