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Manuscript deadline
30 September 2021

Cover image - International Journal of Remote Sensing

International Journal of Remote Sensing

Special Issue Editor(s)

Prof. Yakoub Bazi, King Saud University, Saudi Arabia
[email protected]

Dr. Gabriele Cavallaro, Jülich Supercomputing Centre, Jülich, Germany
[email protected]

Prof. Begüm Demir, Technical University of Berlin, Germany
[email protected]

Prof. Farid Melgani, University of Trento, Italy
[email protected]

Visit JournalArticles

Learning from Data for Remote Sensing Image Analysis

Recent advances in satellite technology have led to a regular, frequent, and high-resolution monitoring of Earth at the global scale, providing an unprecedented amount of Earth observation (EO) data. The growing operational capability of global Earth monitoring from space provides a wealth of information on the state of our planet Earth that waits to be mined for several different EO applications, e.g., climate change analysis, urban area studies, forestry applications, risk and damage assessment, water quality assessment, crop monitoring, etc. Recent studies in machine learning have triggered substantial performance gain for the above-mentioned tasks. This Special Issue aims at gathering a collection of papers in areas interested in learning from data with applications to remote sensing image analysis. Topics of interest in this context include (but are not limited to):

- Deep learning (architectures, generative models, deep reinforcement learning, etc.)
- Explainable and interpretable machine learning
- General machine learning (active learning, clustering, online learning, self-supervised learning, reinforcement learning, semi-supervised learning, unsupervised learning, multi-modal learning etc.)
- Domain adaptation and generalization
- Machine learning and natural language processing
- HPC-based and distributed machine learning for large-scale image analysis
- Quantum computing to speed-up learning optimization problems

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Submission Instructions

Authors are strongly encouraged to:
1) Promote the uptake of fair practices in machine learning (i.e., findable, accessible, interoperable and reusable data)
2) Engage with transparency and open source policies with respect to their methods and codes (e.g., use of Github)
2) Provide a detailed description of the datasets employed in the experimental validation, as well as a comprehensive interpretation of the results in relation to the nature of the data
3) Perform experimental validation also by considering benchmark remote sensing datasets
When you submit your paper in the online submission system, please indicate that it is for a special issue and select “Learning from Data for Remote Sensing Image Analysis” from the drop-down menu.

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