REO: Advances in Representation Learning for Earth Observation
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The 1st REO Workshop @ EurIPS 2025
December 6th or 7th 2025 (Date TBC)
Copenhagen, Denmark
image source: s2maps.eu
About
About
About the REO workshop: The Representation Learning for Earth Observation (REO) workshop brings together researchers and practitioners from machine learning, computer vision, and Earth sciences to advance the development of robust, interpretable, and scalable models for monitoring our planet. With the growing availability of large-scale, multimodal Earth observation (EO) data and the rise of general-purpose foundation models, new opportunities and challenges emerge for integrating data-driven approaches across sensing modalities and application domains. REO will provide a forum for presenting novel technical methods, scientific applications, and system-level innovations, fostering cross-disciplinary exchange and collaborations between academia, industry, and policy stakeholders.
Due to EO's huge potential to tackle pressing societal challenges, there has also been a growing interest from the machine learning and computer vision community in recent years. The development of representation learning approaches within the EO domain has gained interest and momentum beyond academia. Notable recent examples include Google DeepMind’s AlphaEarth, IBM-ESA’s Terramind, AllenAI’s Earth System, and Meta’s DINOv3.
This growing interest calls for increased community exchange around the development, deployment, and the practical use of these models. This workshop aims to discuss the following :
Where are we, and how can we move forward as a community?
What are the open challenges in learning representations of EO data?
In the era of general-purpose one-for-all models, what is the role of specialized approaches?
About the EurIPS conference: EurIPS is a European conference officially endorsed by NeurIPS, the most prestigious AI conferences globally; EurIPS showcases cutting-edge research papers that shape the future of artificial intelligence; EurIPS workshops are independent of NeurIPS workshops; the ELLIS UnConference is the kick-off event of EurIPS and welcomes all participants to join.
More information on the EurIPS website: eurips.cc
Speakers
Speakers
Call for Papers
Call for Papers
We invite participants to present their novel work as extended abstracts or discuss recently published work that is relevant for the workshop. All submissions are handled through the CMT platform and will be reviewed by the program committee. Accepted contributions will be presented as posters (format guidelines coming soon), but authors can indicate their interest of giving an oral talk (10min).
Submission via CMT: COMING SOON
Submission guidelines
Novel unpublished work should be formatted using the NeurIPS template with max. 4 pages single column (excluding references). The submission is double-blind (anonymous authors and reviewers) and non-archival, which means that we will not formally publish the submissions.
Published papers from the last year (after NeurIPS 2024) can be submitted by uploading the formally published paper (single-blind, anonymous reviewers).
Timeline
Submission deadline: October 15, 2025, AoE (Please register your paper early, this helps us to organize the review process)
Notification of acceptance: October 31, 2025, AoE
Camera ready for extended abstracts: November 28, 2025, AoE
Scope
This EurIPS workshop welcomes contributions spanning technical methods, scientific applications, and system-level innovations across EO, environmental monitoring, and related Earth sciences. Topics of interest include (but are not limited to):
Machine Learning and AI for EO: self-supervised, multimodal, and domain-adaptive models; continual and online learning; foundation models tailored to EO; human-in-the-loop and active learning strategies.
Physics-based and Hybrid Modeling: integration of Radiative Transfer Models (RTMs) and other physical simulators into ML pipelines; hybrid AI–physics models for parameter retrieval and uncertainty quantification.
Ecology and Environmental Monitoring: detection and tracking of land use and land cover change, biodiversity and habitat mapping, phenology, biomass and canopy height estimation, soil and vegetation condition assessment.
Remote Sensing and Satellite Data Processing: handling multimodal sources (e.g., multispectral, SAR, LiDAR, hyperspectral), multi-resolution fusion, temporal change detection, and cross-sensor harmonization.
Embeddings and Compression: learned representations for efficient storage, retrieval, and search in large EO archives; semantic compression for scalable analysis without loss of task-relevant information.
Earth Science Applications: geophysical parameter estimation, urban and rural mapping, monitoring of geohazards such as floods, landslides, or volcanic activity.
Data Curation, Bias, and Accessibility: building globally representative, reproducible, and inclusive EO datasets; mitigating spatial and sensor biases; developing open benchmarks and standardizing EO pipelines.
Technical and Use Case Innovations: novel architectures, training strategies, or processing pipelines with demonstrable impact on real-world EO challenges.
Tentative program
Tentative program
Location
Location
Bella Center Copenhagen
Bella Center Copenhagen
Center Blvd. 5
Center Blvd. 5
2300 København S
2300 København S
Denmark
Denmark
Organizers and Program Committee
Organizers and Program Committee
Organizers
Loic Landrieu (ENPC)
Begüm Demir (BIFOLD and TU Berlin)
Nico Lang (University of Copenhagen)
Johannes Jakubik (IBM)
Valerio Marsocci (ESA, Φ-lab)
Ruben Cartuyvels (ESA, Φ-lab)
Hui Zhang (University of Copenhagen)
Program Committee
Jocelyn Chanussot (INRIA)
Charlotte Pelletier (UBS Vannes)
Dino Ienco (INRAE)
Ronny Hänsch (DLR)
Behnood Rasti (BIFOLD, TU Berlin)
Gencer Sumbul (EPFL)
Ribana Roscher (Julich Supercomputing Centre, University of Bonn)
Mikolaj Czerkawski (Asterik Labs)
Nicolas Longépé (ESA, Φ-lab)
Benedikt Blumenstiel (IBM)
Acknowledgement
Acknowledgement
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.
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