LEADER 03326oam 22004815 450 001 9910647486903321 005 20231201222958.0 010 $a981-19-6375-4 024 7 $a10.1007/978-981-19-6375-9 035 $a(CKB)5580000000514049 035 $a(MiAaPQ)EBC7191414 035 $a(Au-PeEL)EBL7191414 035 $a(OCoLC)1369148762 035 $a(DE-He213)978-981-19-6375-9 035 $a(PPN)268209006 035 $a(EXLCZ)995580000000514049 100 $a20230203d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial intelligence oceanography /$fedited by Xiaofeng Li, Fan Wang 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (xii, 346 pages) $cillustrations (chiefly color) 311 0 $a981-19-6374-6 327 $aTheory 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. 330 $aThis 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. 606 $aOceanography$xData processing 606 $aArtificial intelligence 615 0$aOceanography$xData processing. 615 0$aArtificial intelligence. 676 $a551.46 701 $aLi$b Xiaofeng$01324889 701 $aWang$b Fan$0654854 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910647486903321 996 $aArtificial Intelligence Oceanography$93058936 997 $aUNINA