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.

Photos & Impressions

The workshop took place in Vienna on May 11th 2024. Here are some impressions from the Venue.


Thanks to our sponsors, we could award the best paper and best poster.

Best Paper Award Best Poster Award
Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data. Zhang, Chenhui; Wang, Sherrie A Concise Tiling Strategy for Preserving Spatial Context in Earth Observation Imagery. Abrahams, Ellianna; Snow, Tasha; Pérez, Fernando; Siegfried, Matthew


Location Workshop 9 - Room Lehar 4

Time Topic
9:00-9:05 Introduction and Opening Remarks
9:05-9:30 Keynote Speaker #1: Sherrie Wang (MIT)
9:30-10:00 Coffee Break #1 (possibility to set up posters with initial discussions)
10:00-11:15 Poster Session #1
11:15-12:15 Panel - Beyond Benchmarks: Machine Learning for the Planet
12:15-12:45 2 x Orals #1
12:45-14:00 Lunch Break
14:00-14:30 Keynote Speaker #2: Damian Borth (Univ. St Gallen)
14:30-15:00 2 x Orals #2
15:00-16:00 Poster Session & Coffee Break #2
16:00-16:45 3 x Orals #3
16:45-17:00 Closing Remarks and Best Poster/Paper Awards


Orals #1 (12:15-12:45)

Orals #2 (14:30-15:00)

Orals #3 (16:00-16:50)


Presented in Poster Sessions #1 (10:00 - 11:15) & #2 (15:00 - 16:00)

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