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 second 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.

Outcomes from the first ML4RS workshop at ICLR 2023 in Kigali Rwanda can be found here

Call for Papers

Important Dates


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.

Paper Format

We invite short papers describing new and ongoing/in progress research of 4 pages. Page limits do not include references, which are unlimited. Papers will be non-archival. To prepare your submission, please use the LaTeX style files for the ML4RS workshop ICLR 2024. 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.

Machine Learning for Remote Sensing is non-archival and thus dual submission is allowed where permitted by third parties.

Paper Submission

Please submit your paper before the deadline (see important dates!) via CMT on the following submission site:

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.

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 3rd, 11:59pm Anywhere on Earth.

Application form link

Keynote speakers

Sherrie Wang Damian Borth


Ramona Pelich Matej Batic Nico Lang Stefan Lang

Beyond Benchmarks: Machine Learning for the Planet


Program Committee

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


We are looking for more sponsors. Please contact us per email if you would like to support this workshop.

Gold Sponsors

Hyperplan ESA Microsoft

Bronce Sponsors


Technical Sponsors



For questions or information about the Machine Learning for Remote Sensing workshop at ICLR 2024 please contact