Machine Learning for Remote Sensing
This workshop promotes trans-disciplinary research through diverse view-points to tackle the pressing questions of our times, such as climate change, social inequalities, biodiversity, and food security. Developing modern machine learning approaches tailored towards remote sensing data is key to investigating these problems efficiently. This third Machine Learning for Remote Sensing (ML4RS) workshop promotes this exchange by allowing researchers to present their research on environmentally and societally important applications and/or innovative methods that can have an impact in such application domains.
Topics
We solicit research papers addressing advancements in key topics in Machine Learning for Remote Sensing, such as domain adaptation, concept drift, out-of-distribution detection, evaluation using unlabeled data, model architectures for remote sensing data, semi-supervised learning, unsupervised learning, self-supervised learning, multi-fidelity data fusion, federated learning, data-centric AI, human-in-the-loop and active learning, machine learning for time series, methods for learning from limited labeled data (e.g., few-shot learning, meta-learning), new benchmark datasets involving remote sensing data, geographic equity, and fairness.
We welcome applications related to sustainable development, societal needs, planetary exploration, and more, including but not limited to agriculture and food security, forestry, biodiversity and species distribution modeling, natural hazards and disasters, and other societal and environmental questions.
Papers can present methodological innovations designed towards a particular problem or can also apply existing methods when they highlight new perspectives or limitations on existing methods or the broader environmental and societal applicability through a particular dataset or impact area.
Call for Papers
Important Dates
Submission Deadline - February 10, 2025 (Anywhere on Earth)Acceptance Notification - March 3rd, 2025- Travel Support Deadline - March 28th, 2025
- Camera Ready Submission Deadline - April 19, 2025 (Anywhere on Earth)
- Workshop - Sunday April 27th, 2025
Travel Support
Thanks to our sponsors, we have limited funding available to support the travel of students to attend the workshop held at ICLR 2024. Awards are based on merit with additional consideration based on need and travel distance. Priority will be given to those whose papers are accepted for presentation at the workshop.
The deadline for submitting this application is March 28th, 11:59pm Anywhere on Earth.
Paper Format
This year, we have two tracks:
- a workshop paper track (4-pages) and
- a tiny paper track (2-pages)
Please indicate the track in the submission form.
Workshop Paper Track
We invite short papers describing new and ongoing/in progress research of 4 pages. To prepare your submission, please use the LaTeX style files for the ML4RS workshop ICLR 2025 that provides further detail on the paper structure. Paper reviews will be double blind. When submitting your manuscript, make sure you do not include any personally-identifying information such as author names or GitHub links which would de-anonymize the submission.
Page limits do not include references, which are unlimited. The workshop papers will be non-archival and dual submission is allowed where permitted by third parties. After the workshop selected papers will be invited to submit to a special stream in the IEEE Geoscience and Remote Sensing Letters (please note that this will incur a - fast - additional review phase and additional publication fees).
Machine Learning for Remote Sensing is non-archival and thus dual submission is allowed where permitted by third parties.
Tiny Paper Track
Following the ICLR 2023 and 2024 initiatives, we are delighted to implement Tiny Papers in the workshop as part of the DEI initiative at ICLR 2025!
The objectives of the “Tiny Papers” track at ICLR workshops are threefold:
- Creating alternative, complementary, and diverse entry points to research. And in particular, creating approachable avenues for beginners to enter and enjoy the ICLR community.
- Celebrating intermediate breakthroughs in machine learning.
- Efficiently disseminating ideas, findings, and opinions.
Despite the wide range of topics that could benefit from the Tiny Papers format, we focus the type of submissions to be the following:
- An implementation and experimentation of a novel (not published elsewhere) yet simple idea, or a modest and self-contained theoretical result
- A follow-up experiment to or re-analysis of a previously published paper
- A new perspective on a previously published paper
The guidelines and restrictions for this Tiny Papers track are:
Write a paper with the same structure as seen in full papers, but with at most 2 pages of main text. References, Appendices do not count towards the page limit, but we do not recommend dissecting a regular length paper into a tiny paper by moving the majority of the content into appendices. It is recommended to have 1-2 figures, 1 table, and 4-5 sections. But authors are free to use their own structure.
Paper Submission
Please submit your paper before the deadline (see important dates!) via CMT.
- Submission site: https://cmt3.research.microsoft.com/ML4RS2025
You can still edit your submission until the deadline. You can also choose to submit a supplementary pdf alongside the 4-page paper. However, the reviewers are not required to look at the supplementary files.
Keynote Speakers
- Stefano Ermon is an Associate Professor of Computer Science in the CS Department at Stanford University where he is affiliated with the Artificial Intelligence Laboratory. His research is centered on techniques for probabilistic modeling of data and is motivated by applications in the emerging field of computational sustainability. He has won several awards, including multiple Best Paper Awards, a NSF Career Award, ONR and AFOSR Young Investigator Awards, Microsoft Research Fellowship, Sloan Fellowship, and the IJCAI Computers and Thought Award. Stefano earned his Ph.D. in Computer Science at Cornell University in 2015.
- Xiaoxiang Zhu is the Chair Professor for Data Science in Earth Observation at Technical University of Munich (TUM) and was the founding Head of the Department “EO Data Science” at the Remote Sensing Technology Institute, German Aerospace Center (DLR). Since 2022, she serves as the PI and director of the national center of excellence ML4Earth (ml4earth.de). Since May 2020, she is the PI and director of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (ai4eo.de), Munich, Germany. Since October 2020, she also serves as a Director of the Munich Data Science Institute (MDSI), TUM. From 2019 to 2022, Zhu has been a co-coordinator of the Munich Data Science Research School and the head of the Helmholtz Artificial Intelligence – Research Field “Aeronautics, Space and Transport”. Prof. Zhu was a guest scientist or visiting professor at the Italian National Research Council (CNR-IREA), Naples, Italy, Fudan University, Shanghai, China, the University of Tokyo, Tokyo, Japan and University of California, Los Angeles, United States in 2009, 2014, 2015 and 2016, respectively. She is currently a visiting AI professor at ESA’s Phi-lab, Frascati, Italy. Her main research interests are remote sensing and Earth observation, signal processing, machine learning and data science, with their applications in tackling societal grand challenges, e.g. Global Urbanization, UN’s SDGs and Climate Change.
Invited Speakers
- Hao Li is a Lecturer in GIS in the Department of Geography at the National University of Singapore (NUS). He received his PhD (Dr. rer. nat) from GIScience group, Heidelberg University, MS.c degree in Geomatics engineering, and double BS.c degrees in both Geographical Information Systems and Computer Science from Wuhan University, China. His recent research focuses on developing novel spatial representation learning methods with multimodal geospatial data for disaster management and urban analytics.
- Soo Chin Liew is Principal Research Scientist (rank of Associate Professor) and Head of Research at the Center for Remote Imaging, Sensing and Processing (CRISP), National University of Singapore (NUS). He obtained his PhD degree in physics from the University of Arizona in 1989 and did post-doctoral research at the Physics Research Lab, University of California at San Francisco (UCSF) Radiology Department before joining NUS. He has previous research experience in thermoacoustic emission, X-ray transmission and single photon emission computed tomography, nuclear microscopy and image processing. His current research focuses on remote sensing of the earth with expertise in hyperspectral imaging, ocean optics, inland and coastal water quality, atmospheric aerosols, forest fires and land cover change. He is Associate Editor of Frontiers in Remote Sensing, Review Editor of Frontiers in Environmental Science, a member of the Editorial Board of Remote Sensing, and former Associate Editor of SPIE Journal of Applied Remote Sensing (2014 - 2019). He participated as a PI in NASA EO-1 satellite science team working on the Hyperion instrument.
Organizers
- Hannah Kerner (Arizona State University)
- Marc Rußwurm (Wageningen University)
- Hamed Alemohammad (Clark University)
- Gedeon Muhawenayo (Arizona State University)
- Gabriel Tseng (McGill / Mila - Quebec AI institute)
- Ribana Roscher (Forschungszentrum Jülich)
- Ronny Hänsch (German Aerospace Center (DLR) / GRSS)
- Evan Shelhamer (Google DeepMind and University of British Columbia)
- Esther Rolf (University of Colorado, Boulder)
- Mirali Purohit (Arizona State University)
Program Committee
We thank the program committee for being available to review the submitted papers!
- Akram Zaytar (Microsoft)
- Alejandro Coca-Castro (The Alan Turing Institute)
- Anastasia Schlegel (DLR)
- Anna Jungbluth (European Space Agency)
- Anthony Fuller (Carleton University)
- Anthony Vodacek (Rochester Institute of Technology)
- Arthur Ouaknine (McGill University and Mila)
- Begum Demir (TU Berlin)
- Caleb Robinson (Microsoft AI for Good Research Lab)
- Camille Kurtz (Université Paris Cité)
- Caroline Gevaert (University of Twente)
- Charlotte Pelletier (Université de Bretagne du Sud)
- Christian Ayala (Tracasa Instrumental)
- Dalton Lunga (Oak Ridge National Laboratory)
- Dino Ienco (INRAE)
- Ellianna Abrahams (UC Berkeley)
- Gilberto Camara (INPE)
- Homa Ansari (ZEISS Meditec)
- Ioannis Athanasiadis (Wageningen University and Research)
- Jakob Gawlikowski (German Aerospace Center (DLR))
- Jan Dirk Wegner (University of Zurich)
- Javiera Castillo Navarro (Cnam)
- Jocelyn Chanussot (Grenoble Institute of Technology)
- Johannes Dollinger (University of Zurich)
- Johannes Jakubik (IBM Research)
- Jonathan Sullivan (University of Arizona)
- Keiller Nogueira (University of Liverpool)
- Kristof Van Tricht (VITO)
- Loic Landrieu (ENPC)
- Lukas Drees (University of Zurich)
- Marc Rußwurm (Wageningen University)
- Michael Mommert (Stuttgart University of Applied Sciences)
- Nicholas LaHaye (JPL)
- Paolo Fraccaro (IBM UK)
- Philipe Ambrozio Dias (Oak Ridge National Laboratory)
- Ragini Mahesh (German Aerospace Center - DLR)
- Raian Maretto (University of Twente)
- Ramesh Nair (Planet Labs PBC)
- Ritvik Sahajpal (University of Maryland)
- Roberto Interdonato (CIRAD)
- Sandeep Ravindra Tengali (Atlassian)
- Sarang Gupta (Columbia University)
- Steven Lu (Jet Propulsion Laboratory)
- Subit Chakrabarti (Cloud To Street)
- Sylvain Lobry (Université Paris Cité)
- Tanya Nair (Floodbase)
- Vishal Nedungadi (Wageningen University and Research)
- Wenwen Li (Arizona State University)
Sponsorship
We are looking for sponsors to support travel grants, best paper awards, and other workshop activities. If you or your organization is interested in becoming a workshop sponsor, please contact the organizing team using the email below.
Sponsors
We are looking for more sponsors. Please contact us per email if you would like to support this workshop.
Gold Sponsors
Silver Sponsors
Contact
For questions or information about the Machine Learning for Remote Sensing workshop at ICLR 2025 please contact ml4rs_iclr25@googlegroups.com.