Introduction. Camps-Valls, G., Zhu, X. X., Tuia, D., & Reichstein, M. In Deep learning for the Earth Sciences, pages 1–11. John Wiley & Sons, Ltd, 2021. Section: 1 _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119646181.ch1![Introduction [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Machine learning methods are widely used to extract patterns and insights from the ever-increasing data streams from sensory systems. Most machine learning research is somehow deep learning-based and new heights in performance have been reached in virtually all fields of data science, both applied and theoretical. Deep learning in remote sensing has been through three main phases with temporal overlapping: exploration, benchmarking, and Earth observation-driven methodological developments. A vast number of algorithms and network architectures have been developed and applied in the geosciences too. The great majority of applications have to do with estimation of key biogeophysical parameters of interest or forecasting essential climate variables. Deep learning can learn such parameterizations to optimally describe the ground truth that can be observed or generated from detailed and high-resolution models of clouds. The chapter also presents some closing thoughts on the key concepts discussed in the preceding chapters of this book.
@incollection{camps-valls_introduction_2021,
title = {Introduction},
isbn = {978-1-119-64618-1},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119646181.ch1},
abstract = {Machine learning methods are widely used to extract patterns and insights from the ever-increasing data streams from sensory systems. Most machine learning research is somehow deep learning-based and new heights in performance have been reached in virtually all fields of data science, both applied and theoretical. Deep learning in remote sensing has been through three main phases with temporal overlapping: exploration, benchmarking, and Earth observation-driven methodological developments. A vast number of algorithms and network architectures have been developed and applied in the geosciences too. The great majority of applications have to do with estimation of key biogeophysical parameters of interest or forecasting essential climate variables. Deep learning can learn such parameterizations to optimally describe the ground truth that can be observed or generated from detailed and high-resolution models of clouds. The chapter also presents some closing thoughts on the key concepts discussed in the preceding chapters of this book.},
language = {en},
urldate = {2021-08-28},
booktitle = {Deep learning for the {Earth} {Sciences}},
publisher = {John Wiley \& Sons, Ltd},
author = {Camps-Valls, Gustau and Zhu, Xiao Xiang and Tuia, Devis and Reichstein, Markus},
year = {2021},
doi = {10.1002/9781119646181.ch1},
note = {Section: 1
\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119646181.ch1},
keywords = {biogeophysical parameters, climate variables, deep learning, geosciences, machine learning, th observation-driven methodological developments},
pages = {1--11},
}
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