Constellations
Can we protect our satelites by predicting collisions with space debris?
ML can learn to proedict the timings of collision onsets from historical trajectory data while associating and explaining the collision risk through a set of key variables
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Kessler: a Machine Learning Library for Spacecraft Collision Avoidance - DOI
8th European Conference on Space Debris ESA/ESOC, Darmstadt, Germany
Authors: Giacomo Acciarini, Francesco Pinto, Sascha Metz, Sarah Boufelja, Sylvester Kaczmarek, Klaus Merz, José A. Martinez-Heras, Francesca Letizia, Christopher Bridges, Atılım Güneş Baydin
Spacecraft Collision Risk Assessment with Probabilistic Programming - DOI
NeurIPS Workshop 2020 AI for Earth Science
Authors: Giacomo Acciarini, Francesco Pinto, Sascha Metz, Sarah Boufelja, Sylvester Kaczmarek, Klaus Merz, José A. Martinez-Heras, Francesca Letizia, Christopher Bridges, Atılım Güneş Baydin
Towards Automated Satellite Conjunction Management with Bayesian Deep Learning - DOI
Neurips Workshop 2020 ML4PhysicalSciences
Authors: Giacomo Acciarini, Francesco Pinto, Sascha Metz, Sarah Boufelja, Sylvester Kaczmarek, Klaus Merz, José A. Martinez-Heras, Francesca Letizia, Christopher Bridges, Atılım Güneş Baydin
Clouds and Aerosols
How does pollution influence the formation of large marine clouds?
ML can learn to track the motion of mesoscale clouds and find the causal influence of aerosol pollution on complex cloud behaviour
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Tracking Marine Boundary Layer Cloud Transitions Using Machine Learning
AGU 2020
Authors: Matthew Christensen, William Jones, Lucas Kruitwagen, Tim Pearce, Sorawit Saengkyongam, Matt Kusner, Duncan Watson-Parris.
DIGITAL TWIN EARTH
Can we lower the cost of accurate global precipitation forecasts?
ML can learn forecast precipitation by fusing simulated satellite weather data with physical model data, to offer a low-cost alternative to expensive simulation infrastrcuture
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Towards Data-Driven Physics-Informed Global Precipitation Forecasting from Satellite Imagery
Neurips Workshop 2020 Tackling Climate Change with Machine Learning
Authors: Christian Schroeder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Freddie Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr Bilinski
Rainbench: Towards Global Precipitation Forecasting From Satellite Imagery - DOI
AAAI 2020
Authors: Christian Schroeder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Freddie Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr Bilinski
Rainbench: Enabling Data-Driven Precipitation Forecasting On A Global Scale
Neurips Workshop 2020, Tackling Climate Change with Machine Learning
Authors: Christian Schroeder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Freddie Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr BilinskiRainbench: Enabling Data-Driven Precipitation Forecasting On A Global Scale
EGU General Assembly 2021
Authors: Christian Schroeder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Freddie Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr Bilinski
Esa Digital Twin Earth Precursor: Food Systems - DOI
EGU General Assembly 2021
Authors: Taposeea-Fisher, Chandra ; Whitelaw, Alan ; Earl, Jon ; Cullingworth, Christopher ; Jackman, Simon ; Obersteiner, Michael ; Watson-Parris, Duncan ; Gal, Yarin ; Khabarov, Nikolay ; Folberth, Christian ; Orduña-Cabrera, Fernando ; Parr, James ; Silverberg, Leonard
Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization - DOI
IEEE Transactions on Neural Networks and Learning Systems
Authors: Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie, Natalia Díaz-Rodríguez, Océane Boulais, Aruna Sankaranarayanan, Margaux Masson-Forsythe, Aaron Piña, Yarin Gal, Chedy Raïssi, Alexander Lavin, Dava Newman
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