[CFP] 1st Workshop on ML for Remote Sensing at ICLR 2023

[Hannah Kerner]

Hello everyone and happy new year,

We are pleased to announce that the 1st workshop on Machine Learning for Remote Sensing will be held in person at ICLR on May 5th 2023 in Kigali, Rwanda. A virtual participation option will be available.

The Machine Learning for Remote Sensing workshop will provide a platform for researchers to share their work on machine learning applied to remote sensing with a particular focus on challenges raised by applications in the African context, including sparse and noisy labels, high intra-class and low inter-class variability and distribution shift from the global North (where many labels exist).

We invite original research paper submissions from a wide range of topics related to machine learning for remote sensing, including but not limited to:

• Domain adaptation

• Concept drift and out of distribution detection

• Evaluation using unlabeled data

• Model architectures for remote sensing data

• Semi-supervised learning

• Unsupervised learning

• Multi-fidelity data fusion

• Human in the loop and active-learning

• Machine learning for time series

• Methods for learning from limited labeled data (e.g., low-shot learning, meta-learning)

• New benchmark datasets involving remote sensing data

• Geographic equity

• Solutions specific to the African context

• 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, and natural hazards and disasters (e.g., landslides).

Please consider submitting a paper describing your work. We will solicit two types of papers: short proposal papers (3 pages) and short papers describing new and ongoing/in progress research (4 pages). Page limits do not include references, which are unlimited. To prepare your submission, please use the LaTeX style files for ICLR 2023, provided at https://github.com/ICLR/Master-Template/raw/master/iclr2023.zip.

Important dates:

• Submissions are now open via CMT: https://cmt3.research.microsoft.com/ICLRMLRS2023 

• Submissions will close February 3rd, 2023, 11:59 PM AoE. There is no separate deadline for abstracts.

• Withdrawals are possible until March 3rd, 2023.

• Accept/reject notifications will be sent March 3rd, 2023.

• Authors of spotlighted papers will be invited to give a short talk at the workshop and will be notified with details shortly after the acceptance notifications.

• Camera-ready pdfs and posters of accepted submissions are due by April 21st, 2023 11:59 PM AoE.

• Accepted submissions will be posted on the workshop website on April 22nd, 2023, and not before. Rejected or withdrawn submissions will not be posted.

• The workshop will take place in person (with virtual participation option) on May 5th, 2023.

Machine Learning for Remote Sensing is non-archival and thus dual submission is allowed where permitted by third parties. More details can be found here: https://nasaharvest.github.io/ml-for-remote-sensing/iclr2023/#call-for-papers. We will update this page continuously with additional information as it becomes available.

We are also seeking program committee members from the above fields or related areas. If interested, please email ml4rs_...@googlegroups.com.

Should you have any inquiry, feel free to contact us at: ml4rs_...@googlegroups.com 

On behalf of the organizers of Machine Learning for Remote Sensing, we are looking forward to your submissions and participation!

- Organizing team (Hannah Kerner, Catherine Nakalembe, Gabriel Tseng, Ivan Zvonkov, Gedeon Muhawenayo, Moise Busogi, Ange Tesire Marie, Hamed Alemohammad)