Abstract
The twenty-seven articles in this special issue is a follow-up to special sessions organized at WHISPERS conferences. Such sessions drew unexpectedly large attendance, signaling the interest and need for a focused platform to exchange knowledge at the intersection of machine learning and remote sensing. The collection of papers in this special issue presents a comprehensive sample of the latest trends in the design of machine learning algorithms for geospatial data. It covers a wide spectrum of remote sensing applications and presents new solutions to answer to the call of the new generation of sensors, covering the electromagnetic range from optical to microwave data.
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