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RESEARCH PRODUCT
A Special Issue on Advances in Machine Learning for Remote Sensing and Geosciences [From the Guest Editors]
Gustau Camps-vallsMelba M. CrawfordJose M. Bioucas-diassubject
Data processingGeneral Computer Sciencebusiness.industryComputer science020206 networking & telecommunications02 engineering and technologyMachine learningcomputer.software_genreData scienceField (computer science)Earth system scienceKnowledge extractionRemote sensing (archaeology)0202 electrical engineering electronic engineering information engineeringGeneral Earth and Planetary Sciences020201 artificial intelligence & image processingArtificial intelligenceElectrical and Electronic EngineeringbusinessInstrumentationcomputerRemote sensingConstellationdescription
Machine learning has become a standard paradigm for the analysis of remote sensing and geoscience data at both local and global scales. In the upcoming years, with the advent of new satellite constellations, machine learning will have a fundamental role in processing large and heterogeneous data sources. Machine learning will move from mere statistical data processing to actual learning, understanding, and knowledge extraction. The ambitious goal is to provide responses to the challenging scientific questions about the earth system. This special issue aims at providing an updated, refreshing view of current developments in the field. For this special issue, we have collected five articles that present snapshots of the recent advances in machine-learning methodologies for remote sensing and geosciences.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-06-01 | IEEE Geoscience and Remote Sensing Magazine |