Permanence – Clinique des Données
13 novembre 2025 @ 14 h 00 min – 16 h 00 min –
Campus Genopolys, Salle Rotonde
Institut de Science des Données de Montpellier
La Clinique des données est un service mis en place par l’ISDM afin de porter assistance à la communauté scientifique sur des thématique liées aux données. Ainsi, toute personne ayant une problématique, une question, un bug est la bienvenue lors des permanences de ce service. Vous serez accueilli par des experts en IA, en (Bio)Informatique et en (Bio)Statistique.
Accompagnement, Assistance
Deep Learning for Species Recognition under High Uncertainty: Application to jellyfish images
13 novembre 2025 @ 14 h 00 min – 15 h 00 min –
Building 5, 02.124, Campus St Priest
Machine Learning in Montpellier, Theory & Practice – Matthieu de Castelbajac (Univ. Montpellier)
Citizen science records are a valuable source of biodiversity data, and even more essential to help track mobile marine species like jellyfish. However, these records can be highly uncertain, containing many potential errors and biases. They are typically validated by experts, which is impractical at scale. Although deep learning methods for automatic validation have shown promising results, they fail to account for the uncertainty present in both the input data and their predictions. Here, we present a semi-automated method to support record validation at scale while providing strong statistical guarantees, including for highly uncertain citizen science records.
IA et Experts
Deep Learning for Species Recognition under High Uncertainty: Application to jellyfish images
13 novembre 2025 @ 14 h 00 min –
Room 02.124, Building 5, St Priest campus
Machine Learning in Montpellier, Theory & Practice – Matthieu de Castelbajac (Univ. Montpellier)
Citizen science records are a valuable source of biodiversity data, and even more essential to help track mobile marine species like jellyfish. However, these records can be highly uncertain, containing many potential errors and biases. They are typically validated by experts, which is impractical at scale. Although deep learning methods for automatic validation have shown promising results, they fail to account for the uncertainty present in both the input data and their predictions. Here, we present a semi-automated method to support record validation at scale while providing strong statistical guarantees, including for highly uncertain citizen science records.
Machine Learning in Montpellier, Theory & Practice
Seeing the forest and the trees: How hyperspatial drone imagery is revolutionizing tropical canopy studies (CS focus)
13 novembre 2025 @ 14 h 00 min –
Amphi Moreau, Building 2, St Priest campus
Machine Learning in Montpellier, Theory & Practice – Étienne Laliberté (Univ. Montréal / MILA)
Tropical forests hold the majority of terrestrial plant carbon and biodiversity, but they are being altered with climate change. However, we do not know how the vast majority of tropical tree species are responding to climate change and other stressors because traditional field-based approaches cannot collect sufficiently large sample sizes for most species. As part of the winning team of the XPRIZE Rainforest competition, we have developed an AI solution using drone imagery that can greatly accelerate the mapping of tropical trees. The drone hardware needed is affordable and readily accessible to researchers and conservation agencies. In this talk, I will present this technology, which I will argue has the potential to revolutionize tropical forest science, conservation, and restoration. I will talk about the opportunities, as well as some challenges that need to be addressed to unlock the potential of this technology for tropical canopy studies.,,
Machine Learning in Montpellier, Theory & Practice