Towards a Universal Representation of Earth Observation
3 juillet 2025 @ 14 h 00 min –
Room 02.124, Building 5, St Priest campus
Machine Learning in Montpellier, Theory & Practice – Loic Landrieu (LIGM, ENPC, IP Paris, Univ Gustave Eiffel)
Machine Learning in Montpellier, Theory & Practice
Simulating Environment-Conditioned Seabird Trajectories with Generative AI
3 juillet 2025 @ 14 h 00 min –
Room 02.124, Building 5, St Priest campus
Machine Learning in Montpellier, Theory & Practice – Julien Patras (IRD / Marbec)
Seabirds serve as key bioindicators of ocean health, and with modern tracking technologies like GPS tags, we now have rich datasets capturing their offshore movements. These trajectory data enable us to study behavioral states and identify ecologically significant foraging zones. However, modeling how environmental variables (like wind, sea surface temperature, or currents) shape these trajectories remains largely unexplored. Trajectory simulation using generative artificial intelligence has shown great promise in fields like autonomous driving and human motion modeling. Inspired by these advances, we introduce a novel approach in movement ecology by adapting DiffTraj (Zhu et al., 2023), a pretrained diffusion model originally developed for vehicle trajectories. We then adapt and fine-tune the model on seabird movement data, so that it supports multi-species, multi-site simulation, with the aim of environmental conditioned generation, and improving state-of-the-art seabird trajectory simulation.
Machine Learning in Montpellier, Theory & Practice
Machine Learning in Montpellier, Theory & Practice
3 juillet 2025 @ 15 h 30 min –
Campus St Priest (860 Rue Saint Priest 34095 Montpellier Cedex 5), bat. 5, Room: 02.124
Machine Learning in Montpellier, Theory & Practice – Pedro Valdeira
Julien Patras (IRD / Marbec) will give a talk : Seabirds serve as key bioindicators of ocean health, and with modern tracking technologies like GPS tags, we now have rich datasets capturing their offshore movements. These trajectory data enable us to study behavioral states and identify ecologically significant foraging zones. However, modeling how environmental variables (like wind, sea surface temperature, or currents) shape these trajectories remains largely unexplored. Trajectory simulation using generative artificial intelligence has shown great promise in fields like autonomous driving and human motion modeling. Inspired by these advances, we introduce a novel approach in movement ecology by adapting DiffTraj (Zhu et al., 2023),a pretrained diffusion model originally developed for vehicle trajectories. We then adapt and fine-tune the model on seabird movement data, so that it supports multi-species, multi-site simulation, with the aim of environmental conditioned generation, and improving state-of-the-art seabird trajectory simulation.
Seabirds, Trajectories, Environmental variables, LabéliséHallesIA