Machine Learning Methods for Polar Regions

Dear Colleagues,

Polar regions are remote and hostile environments where efforts to collect in situ observations and data are limited by very real constraints, such as the weather, lack of infrastructure, and long periods of polar darkness during winter. Consequently, satellite platforms provide the only source of consistent, repeatable, regional-scale, year-round data of the polar regions, but parameters extracted from them still lack the necessary veracity.

This Special Issue aims to put together contributions from the ExtremeEarth and AI4Arctic projects, but also other similar projects.

We cordially invite researchers who have an interest in this topic to submit their original papers. Potential topics for this Special Issue are related (but not limited only) to the following topics:

• Artificial intelligence/machine learning/deep learning techniques
• Copernicus and third-party data
• Big data
• Fusion data
• Earth Observation training data
• Sea-ice charts
• Linked geospatial data
• Spatiotemporal evolution patterns
• Semantic multisensor satellite image time series analysis
• Knowledge and extreme earth analytics

Dr. Corneliu Octavian Dumitru
Dr. Andrea Marinoni
Mr. Tore Wulf
Guest Editors

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