[CFP] 4th Workshop on ML for Remote Sensing at ICLR 2026

[Hannah Kerner]

Hello everyone,

We are pleased to announce that we will host the 4th workshop on Machine Learning for Remote Sensing at ICLR 2026. It will take place in person in Rio de Janeiro, Brazil on April 26 or 27, 2026. 

This workshop promotes cross-disciplinary research through diverse viewpoints 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. The special theme of our workshop this year is “ML4RS: publication to practice”. Our workshop this year will focus on bridging gaps between research and real-world applications while continuing to catalyze state-of-the-art research through discussion and feedback on early-stage research.

This year we have several options for submission (all are non-archival) with a deadline of February 6, 2026 (AoE):

• Short research papers (4 pages) describing new and ongoing/in progress research. 
• Tiny papers (2 pages) that present focused contributions at the intersection of machine learning and remote sensing.
• 🆕 This year’s workshop features a new Tutorials track, which aims to expedite the use of new models, code libraries, datasets, and benchmark challenges – facilitating their use in both practical applications and comprehensive benchmarking in future research studies. We invite short papers (up to 4 pages) detailing a tutorial for a model, code library, dataset, or other contribution. We expect most (but not necessarily all) tutorials will be accompanied by an executable Colab notebook, Jupyter notebook, or other code files that can be run on a laptop.

We hope to see you there and look forward to your submissions,

- Hannah Kerner, on behalf of all organizers Esther Rolf, Bianca Zadrozny, Gabriel Tseng, Marc Rußwurm, Ronny Hänsch, Hamed Alemohammad, and Evan Shelhamer

- Hannah

Hannah Kerner, PhD

Assistant Professor

School of Computing and Augmented Intelligence (SCAI)

Arizona State University

AI/ML Lead, NASA Harvest

https://hannah-rae.github.io/