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

Paper Format

This year, we have two tracks:

  1. a workshop paper track (4-pages) and
  2. 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:

Despite the wide range of topics that could benefit from the Tiny Papers format, we focus the type of submissions to be the following:

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.

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

Organizers

Program Committee

We thank the program committee for being available to review the submitted papers!

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.

Contact

For questions or information about the Machine Learning for Remote Sensing workshop at ICLR 2025 please contact ml4rs_iclr25@googlegroups.com.