Author

M. Volpi

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Open access Aug 2026

CoDiT: conditional diffusion models for multi-day ocean drifter trajectory prediction

This work frames trajectory prediction as a denoising task and presents Conditional Diffusion models for Trajectories (CoDiT), which adapts the denoising diffusion framework, originally developed for image synthesis, to the problem of trajectory forecasting.

Christian Donner, Shirin Goshtasbpour, Emanuele Dalsasso et al. · Machine Learning: Earth · 0 citations

Preprint Aug 2026

Learning to Forecast Crop Growth from Earth Observation Data

Forecasting crop growth across agricultural landscapes is important for improving the productivity, resilience, and operational management of farming systems. In this work, we investigate whether Earth observation time series and meteorological drivers can be used to predict future canopy development at country scale....

Dominik Senti, Mehmet Ozgur Turkoglu, M. Volpi et al. · 0 citations