22 citations · 58 across the 8 of their papers we have counts for
8 papers
How Can Machine Learning Emulators Best Support Climate Science?
Luca Schmidt, Nina Effenberger, Vitus Benson +5
For decades, physics-based climate models have been used to provide insights for climate decision-making. Their application is, however, constrained by significant computational an…
Atmospheric Transport Modeling of CO with Neural Networks
Vitus Benson, Ana Bastos, Christian Reimers +3
Accurately describing the distribution of CO in the atmosphere with atmospheric tracer transport models is essential for greenhouse gas monitoring and verification support syst…
DeepExtremeCubes: Integrating Earth system spatio-temporal data for impact assessment of climate extremes
Chaonan Ji, Tonio Fincke, Vitus Benson +12
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show prom…
Multi-modal learning for geospatial vegetation forecasting
Vitus Benson, Claire Robin, Christian Requena-Mesa +7
The innovative application of precise geospatial vegetation forecasting holds immense potential across diverse sectors, including agriculture, forestry, humanitarian aid, and carbo…
Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs
Claire Robin, Christian Requena-Mesa, Vitus Benson +4
Forecasting the state of vegetation in response to climate and weather events is a major challenge. Its implementation will prove crucial in predicting crop yield, forest damage, o…
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task
Christian Requena-Mesa, Vitus Benson, Markus Reichstein +2
Satellite images are snapshots of the Earth surface. We propose to forecast them. We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on futu…