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Niccolò Maffezzoli

@nicomaffe.bsky.social

Machine Learning | Earth Science UC Irvine, University of Venice

Niccolò Maffezzoli @nicomaffe.bsky.social · 25/11/2025

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.

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Niccolò Maffezzoli @nicomaffe.bsky.social · 17/05/2025

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.

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Niccolò Maffezzoli @nicomaffe.bsky.social · 09/05/2025

Glacier ice thickness maps produced globally with a machine learning system. @ucirvine.bsky.social @unive Ca' Foscari gmd.copernicus.org/articles/18/...

gmd.copernicus.org

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...

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