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UID:347@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20250703T140000
DTEND;TZID=Europe/Paris:20250703T140000
DTSTAMP:20260828T092739Z
URL:https://isdm.umontpellier.fr/events/simulating-environment-conditioned
 -seabird-trajectories-with-generative-ai-3/
SUMMARY:Simulating Environment-Conditioned Seabird Trajectories with Genera
 tive AI
DESCRIPTION:Room 02.124\, Building 5\, St Priest campus\n\nMachine Learning
  in Montpellier\, Theory &amp\; Practice - Julien Patras (IRD / Marbec)\n\
 nSeabirds serve as key bioindicators of ocean health\, and with modern tra
 cking technologies like GPS tags\, we now have rich datasets capturing the
 ir offshore movements. These trajectory data enable us to study behavioral
  states and identify ecologically significant foraging zones. However\, mo
 deling how environmental variables (like wind\, sea surface temperature\, 
 or currents) shape these trajectories remains largely unexplored. Trajecto
 ry simulation using generative artificial intelligence has shown great pro
 mise 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 origi
 nally 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.\n\nMachine L
 earning in Montpellier\, Theory &amp\; Practice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
LOCATION:Room 02.124 Building 5\, St Priest Campus\, Montpellier\, 34000\, 
 France
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 ier\, 34000\, France;X-APPLE-RADIUS=100;X-TITLE=Room 02.124 Building 5:geo
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