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Artificial intelligence oceanography

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence oceanographyedited by Xiaofeng Li, Fan Wang.
other author:	Li, Xiaofeng.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	xii, 346 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	OceanographyData processing.
Online resource:	https://doi.org/10.1007/978-981-19-6375-9
ISBN:	9789811963759$q(electronic bk.)

Artificial intelligence oceanography
Artificial intelligence oceanography[electronic resource] /edited by Xiaofeng Li, Fan Wang. - Singapore :Springer Nature Singapore :2023. - xii, 346 p. :ill. (chiefly col.), digital ;24 cm.

Theory and technology of artificial intelligence for oceanography -- Satellite data-driven internal wave forecast model based on machine learning techniques -- Detection and analysis of marine macroalgae based on artificial intelligence -- Tropical cyclone intensity estimation from geostationary satellite imagery -- Reconstructing marine environmental data based on deep learning -- Detecting oceanic processes from space-borne sar imagery using machine learning -- Deep convolutional neural networks-based coastal inundation mapping for un-defined least developed countries: taking madagascar and mozambique as examples -- Ai- based mesoscale eddy study -- Classifying sea ice types from sar images based on deep fully convolutional networks -- Detecting ships and extracting ship's size from SAR images based on deep learning -- Quality control of ocean temperature and salinity data based on machine learning technology -- automatic extraction of internal wave signature from multiple satellite sensors based on deep convolutional neural networks -- Automatic extraction of waterlines from large-scale tidal flats on SAR images and applications based on deep convolutional neural networks -- Forecast of tropical instability waves using deep learning -- Sea surface height prediction based on artificial intelligence.

Open access.

This open access book invites readers to learn how to develop artificial intelligence (AI)-based algorithms to perform their research in oceanography. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing.

ISBN: 9789811963759$q(electronic bk.)

Standard No.: 10.1007/978-981-19-6375-9doiSubjects--Topical Terms:

379703
Oceanography
--Data processing.

LC Class. No.: GC10.4.E4 / A77 2023

Dewey Class. No.: 551.46028563

Artificial intelligence oceanography
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245 0 0 $a Artificial intelligence oceanography $h [electronic resource] / $c edited by Xiaofeng Li, Fan Wang.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xii, 346 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
505 0 $a Theory and technology of artificial intelligence for oceanography -- Satellite data-driven internal wave forecast model based on machine learning techniques -- Detection and analysis of marine macroalgae based on artificial intelligence -- Tropical cyclone intensity estimation from geostationary satellite imagery -- Reconstructing marine environmental data based on deep learning -- Detecting oceanic processes from space-borne sar imagery using machine learning -- Deep convolutional neural networks-based coastal inundation mapping for un-defined least developed countries: taking madagascar and mozambique as examples -- Ai- based mesoscale eddy study -- Classifying sea ice types from sar images based on deep fully convolutional networks -- Detecting ships and extracting ship's size from SAR images based on deep learning -- Quality control of ocean temperature and salinity data based on machine learning technology -- automatic extraction of internal wave signature from multiple satellite sensors based on deep convolutional neural networks -- Automatic extraction of waterlines from large-scale tidal flats on SAR images and applications based on deep convolutional neural networks -- Forecast of tropical instability waves using deep learning -- Sea surface height prediction based on artificial intelligence.
506 $a Open access.
520 $a This open access book invites readers to learn how to develop artificial intelligence (AI)-based algorithms to perform their research in oceanography. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing.
650 0 $a Oceanography $x Data processing. $3 379703
650 0 $a Artificial intelligence. $3 194058
650 1 4 $a Ocean Sciences. $3 914951
650 2 4 $a Atmospheric Science. $3 919200
650 2 4 $a Geographical Information System. $3 913165
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Sustainability. $3 376755
700 1 $a Li, Xiaofeng. $3 785372
700 1 $a Wang, Fan. $3 946254
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856 4 0 $u https://doi.org/10.1007/978-981-19-6375-9
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