Special Issue on "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
- Prof. Yakoub Bazi, King Saud University, Saudi Arabia (email@example.com)
- Dr. Gabriele Cavallaro, Jülich Supercomputing Centre, Jülich, Germany (firstname.lastname@example.org)
- Prof. Begüm Demir, Technical University of Berlin, Germany (email@example.com)
- Prof. Farid Melgani, University of Trento, Italy (firstname.lastname@example.org)