東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Artificial intelligence oceanography

Li, Xiaofeng.

Linked to FindBook

Google Book

Amazon

博客來

Artificial intelligence oceanography

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence oceanography/ edited 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.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Oceanography - Data processing. -
Online resource:	https://doi.org/10.1007/978-981-19-6375-9
ISBN:	9789811963759

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

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

3457180
Oceanography
--Data processing.

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

Dewey Class. No.: 551.46028563

Artificial intelligence oceanography
LDR:02932nmm a2200337 a 4500 001 2315735
003 DE-He213
005 20230203112516.0
006 m d
007 cr nn 008maaau
008 230902s2023 si s 0 eng d
020 $a 9789811963759 $q (electronic bk.)
020 $a 9789811963742 $q (paper)
024 7 $a 10.1007/978-981-19-6375-9 $2 doi
035 $a 978-981-19-6375-9
040 $a GP $c GP
041 0 $a eng
050 4 $a GC10.4.E4 $b A77 2023
072 7 $a RBKC $2 bicssc
072 7 $a SCI052000 $2 bisacsh
072 7 $a RBKC $2 thema
082 0 4 $a 551.46028563 $2 23
090 $a GC10.4.E4 $b A791 2023
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 3457180
650 0 $a Artificial intelligence. $3 516317
650 1 4 $a Ocean Sciences. $3 3592228
650 2 4 $a Atmospheric Science. $3 3592263
650 2 4 $a Geographical Information System. $3 3538564
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Sustainability. $3 1029978
700 1 $a Li, Xiaofeng. $3 3242017
700 1 $a Wang, Fan. $3 716262
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-19-6375-9
950 $a Earth and Environmental Science (SpringerNature-11646)