The seminar of OBELIX team is currently held on thursdays 11:30 am, every two weeks, at the IRISA lab, Tohannic campus (bat. ENSIBS). Usually, the presentation lasts 30 min and is followed by a discussion with the team.
The seminar is coordinated by Yann CABANES: Please contact me for any information or if you want to present your work to our team.
Previous seminars
2019 / 2019-20 / 2020-21 / 2021-22 / 2022-23 / 2023-24 / 2024-25 / 2025-26
Upcoming seminars (2025-26)
- Date: Thursday, December 11, 2025 at 2 p.m.
- Room: A102
- Speaker: Manon Béchaz
- Title: Monitoring a Changing World: Towards Adapting Change Detection in Remote Sensing
- Abstract: The increasing availability of high-resolution satellite and aerial imagery offers unprecedented opportunities to monitor a rapidly transforming world. Yet the types of changes that are interesting to detect – urban expansion, deforestation, flood impact, seasonal variations, etc – are highly contextual and evolve over time. Conventional change detection pipelines, which rely on extensive labeled datasets and are trained for a fixed, predefined set of change categories, are therefore fundamentally limited. They lack the capacity to adapt to new environments or new types of change without costly data labeling and retraining. This motivates the development of adaptive change detection models that can continuously improve from incoming, largely unlabeled data while remaining flexible to evolving tasks. We will explore in this presentation different methods to achieve such adaptive change detection models. We will begin with 2Player, a cooperative self-supervised framework that can transform any existing supervised change detection model into an unsupervised one, enabling adaptation of existing architectures to new data and tasks while addressing the problem of label scarcity. Building on this, we will investigate how a model’s notion of change can be enriched and expanded as new semantic categories or finer-grained distinctions become relevant. We will discuss ongoing work on incremental semantic change detection, motivated by the hierarchical structure of remote sensing imagery, highlighting pathways toward continuously improving change detection systems.