Unlocking Environmental Solutions: Machine Learning for Remote Sensing Workshop
In a world facing challenges like climate change, social inequalities, biodiversity loss, and food security, trans-disciplinary research is crucial. The second Machine Learning for Remote Sensing (ML4RS) workshop aims to foster diverse viewpoints and innovative machine learning approaches tailored for remote sensing data. This intersection of technology and environmental science provides a platform for researchers to present their work on applications impacting society and the environment.
Background:
Building on the success of the first ML4RS workshop at ICLR 2023 in Kigali, Rwanda, the upcoming workshop in Vienna on May 11, 2024, at the Vienna Exhibition & Congress Center promises to be an intellectual hub for researchers and experts. The outcomes from the inaugural event can be explored [here](link to outcomes).
Call for Papers:
Researchers are invited to submit papers addressing advancements in various ML for Remote Sensing topics. The timeline for submissions is as follows:
- Submission Deadline: February 3rd, 2024 (11:59 pm AoE)
- Acceptance Notification: March 3rd, 2024
Workshop Topics:
The workshop focuses on key topics 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.
Applications related to sustainable development, societal needs, planetary exploration, and more are welcomed. Specific areas include agriculture and food security, forestry, biodiversity and species distribution modeling, natural hazards and disasters, and other societal and environmental questions.
Paper Submission Guidelines:
- Papers should be short, describing new or ongoing research within 4 pages (excluding unlimited references).
- Use the LaTeX style files for ICLR 2024 to prepare submissions.
- Papers will be non-archival, and reviews will be double-blind. Ensure submissions do not include personally-identifying information to maintain anonymity.
Dual Submission Policy:
ML for Remote Sensing is non-archival, allowing dual submissions where permitted by third parties.
Unlock the potential of Machine Learning for Remote Sensing in addressing real-world challenges. Researchers are invited to contribute groundbreaking ideas and methodologies in a 4-page paper for the upcoming workshop in Vienna. From domain adaptation to societal equity, this workshop covers diverse topics crucial for environmental and societal impact. Submissions, following the guidelines provided, are accepted until February 3rd, 2024. Join the global discourse on sustainable development, planetary exploration, and more. Explore the outcomes of the previous workshop here and be part of the ML4RS workshop shaping the future of remote sensing and environmental solutions.
