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Artificial intelligence oceanography / Xiaofeng Li (2023)  

Public Titre : Artificial intelligence oceanography Type de document : Monographie Auteurs : Xiaofeng Li, Éditeur scientifique ; Fan Wang, Éditeur scientifique Editeur : Springer Nature Année de publication : 2023 Importance : 346 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-981-19637-5-9 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] algue [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] cyclone [Termes IGN] détection d'objet [Termes IGN] iceberg [Termes IGN] intelligence artificielle [Termes IGN] océanographie [Termes IGN] température de surface de la mer Résumé : (éditeur) 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. Note de contenu : 1- Artificial Intelligence Foundation of smart ocean 2- Forecasting tropical instability waves based on artificial intelligence 3- Sea surface height anomaly prediction based on artificial intelligence 4- Satellite data-driven internal solitary wave forecast based on machine learning techniques 5- AI-based subsurface thermohaline structure retrieval from remote sensing observations 6- Ocean heat content retrieval from remote sensing data based on machine learning 7- Detecting tropical cyclogenesis using broad learning system from satellite passive microwave observations 8- Tropical cyclone monitoring based on geostationary satellite imagery 9- Reconstruction of pCO2 data in the Southern ocean based on feedforward neural network 10- Detection and analysis of mesoscale eddies based on deep learning 11- Deep convolutional neural networks-based coastal inundation mapping from SAR imagery: with one application case for Bangladesh, a UN-defined least developed country 12- Sea ice detection from SAR images based on deep fully convolutional networks 13- Detection and analysis of marine green algae based on artificial intelligence 14- Automatic waterline extraction of large-scale tidal flats from SAR images based on deep convolutional neural networks 15- Extracting ship’s size from SAR images by deep learning 16- Benthic organism detection, quantification and seamount biology detection based on deep learning Numéro de notice : 24105 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Monographie DOI : 10.1007/978-981-19-6375-9 En ligne : https://link.springer.com/book/10.1007/978-981-19-6375-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103058

Deep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination / Frederik Hass in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 12 (December 2020) . - n° 758 Titre : Deep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination Type de document : Article/Communication Auteurs : Frederik Hass, Auteur ; Jamal Jokar Arsanjani, Auteur Année de publication : 2020 Article en page(s) : n° 758 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] Groenland [Termes IGN] hydrocarbure [Termes IGN] iceberg [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] navire [Termes IGN] océan [Termes IGN] seuillage d'image [Termes IGN] trafic maritime Résumé : (auteur) Synthetic aperture radar (SAR) plays a remarkable role in ocean surveillance, with capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night, regardless of clouds and extreme weather conditions. The detection of ocean objects using SAR relies on well-established methods, mostly adaptive thresholding algorithms. In most waters, the dominant ocean objects are ships, whereas in arctic waters the vast majority of objects are icebergs drifting in the ocean and can be mistaken for ships in terms of navigation and ocean surveillance. Since these objects can look very much alike in SAR images, the determination of what objects actually are still relies on manual detection and human interpretation. With the increasing interest in the arctic regions for marine transportation, it is crucial to develop novel approaches for automatic monitoring of the traffic in these waters with satellite data. Hence, this study aims at proposing a deep learning model based on YoloV3 for discriminating icebergs and ships, which could be used for mapping ocean objects ahead of a journey. Using dual-polarization Sentinel-1 data, we pilot-tested our approach on a case study in Greenland. Our findings reveal that our approach is capable of training a deep learning model with reliable detection accuracy. Our methodical approach along with the choice of data and classifiers can be of great importance to climate change researchers, shipping industries and biodiversity analysts. The main difficulties were faced in the creation of training data in the Arctic waters and we concluded that future work must focus on issues regarding training data. Numéro de notice : A2020-808 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9120758 Date de publication en ligne : 19/12/2020 En ligne : https://doi.org/10.3390/ijgi9120758 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96953 [article]

A data model for moving regions of fixed shape in databases / Florian Heinz in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 9-10 (September - October 2018) . - pp 1737 - 1769 Titre : A data model for moving regions of fixed shape in databases Type de document : Article/Communication Auteurs : Florian Heinz, Auteur ; Ralf Hartmut Güting, Auteur Année de publication : 2018 Article en page(s) : pp 1737 - 1769 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] iceberg [Termes IGN] modèle conceptuel de données localisées [Termes IGN] modélisation spatio-temporelle [Termes IGN] objet mobile [Termes IGN] rotation d'objet [Termes IGN] système de gestion de base de données Résumé : (Auteur) Moving object databases are designed to store and process spatial and temporal object data. An especially useful moving object type is a moving region, which consists of one or more moving polygons suitable for modeling the spread of forest fires, the movement of clouds, spread of diseases and many other real-world phenomena. Previous implementations usually allow a changing shape of the region during the movement; however, the necessary restrictions on this model result in an inaccurate interpolation of rotating objects. In this paper, we present an alternative approach for moving and rotating regions of fixed shape, called Fixed Moving Regions, which provide a significantly better model for a wide range of applications like modeling the movement of oil tankers, icebergs and other rigid structures. Furthermore, we describe and implement several useful operations on this new object type to enable a database system to solve many real-world problems, as for example collision tests, projections and intersections, much more accurate than with other models. Based on this research, we also implemented a library for easy integration into moving objects database systems, as for example the DBMS Secondo (1) (2) developed at the FernUniversität in Hagen. Numéro de notice : A2018-303 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1458103 Date de publication en ligne : 04/05/2018 En ligne : https://doi.org/10.1080/13658816.2018.1458103 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90446 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2018051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Sea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: a case study / Lei Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4524 - 4533 Titre : Sea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: a case study Type de document : Article/Communication Auteurs : Lei Wang, Auteur ; K. Andrea Scott, Auteur ; Linlin Xu, Auteur ; David A. Clausi, Auteur Année de publication : 2016 Article en page(s) : pp 4524 - 4533 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] classification par réseau neuronal [Termes IGN] eau de fonte [Termes IGN] glace de mer [Termes IGN] iceberg [Termes IGN] image Radarsat [Termes IGN] navigation maritime [Termes IGN] réseau neuronal convolutif Résumé : (Auteur) High-resolution ice concentration maps are of great interest for ship navigation and ice hazard forecasting. In this case study, a convolutional neural network (CNN) has been used to estimate ice concentration using synthetic aperture radar (SAR) scenes captured during the melt season. These dual-pol RADARSAT-2 satellite images are used as input, and the ice concentration is the direct output from the CNN. With no feature extraction or segmentation postprocessing, the absolute mean errors of the generated ice concentration maps are less than 10% on average when compared with manual interpretation of the ice state by ice experts. The CNN is demonstrated to produce ice concentration maps with more detail than produced operationally. Reasonable ice concentration estimations are made in melt regions and in regions of low ice concentration. Numéro de notice : A2016-886 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2543660 En ligne : https://doi.org/10.1109/TGRS.2016.2543660 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83066 [article]

Reducing leakage error in GRACE-observed long-term ice mass change: a case study in West Antarctica / J. L. Chen in Journal of geodesy, vol 89 n° 9 (september 2015)  

Public [article]  inJournal of geodesy > vol 89 n° 9 (september 2015) . - pp 925 - 940 Titre : Reducing leakage error in GRACE-observed long-term ice mass change: a case study in West Antarctica Type de document : Article/Communication Auteurs : J. L. Chen, Auteur ; C. R. Wilson, Auteur ; Jin Li, Auteur ; Zizhan Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 925 - 940 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] Antarctique [Termes IGN] données GRACE [Termes IGN] erreur systématique [Termes IGN] force de gravitation [Termes IGN] harmonique sphérique [Termes IGN] iceberg [Termes IGN] masse [Termes IGN] zone polaire Résumé : (auteur) Spatial leakage is a major limitation for quantitative interpretation of satellite gravity measurements from the gravity recovery and climate experiment (GRACE). Using synthetic data to simulate ice mass changes in the Amundsen Sea Embayment and Antarctic Peninsula, we analyze quantitatively the effects of a limited range of spherical harmonics (SH) coefficients and additional filtering, which in combination can significantly attenuate signal amplitudes. We present details of a forward modeling algorithm and show that it is capable of removing these biases from GRACE estimates. Examples show how to implement the method by constraining locations of presumed mass changes, or leaving these locations unspecified within a continental region. Our analysis indicates that leakage effects from far-field mass signals (e.g., terrestrial water storage change and glacial melting over other continents) on Antarctic mass rate estimates appear to be negligible. However, leakage from long-term ocean bottom pressure change in the surrounding Antarctic Circumpolar Current regions may bias Antarctic mass rate estimates by up to 20 Gigatonne per year (Gt/year). Experiments based on proxy GRACE measurement noise indicate that the effects of GRACE spatial noise on estimated Antarctic mass rates via constrained and unconstrained forward modelings are ∼5 and 15 Gt/year, respectively. Numéro de notice : A2015-877 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-015-0824-2 Date de publication en ligne : 22/05/2015 En ligne : https://doi.org/10.1007/s00190-015-0824-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79407 [article]

MIZEX [Marginal Ice Zone Experiment] 1984 Varan-S data set / N. Lannelongue (1985)

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The importance of satisfactory positioning, diving and mapping systems, suitable for exploration and transportation in ice-covered sea areas / Ragnar Thoren (1982)

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Le radar latéral pour la détection des nappes d'huile et l'étude de l'état de la mer / D. Staerke in Bulletin [Société Française de Photogrammétrie et Télédétection], n° 79 - 80 (Octobre 1980)

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Remote sensing as an aid for navigation in ice-covered sea areas, Paper for the ISP Symposium, Commission 7, Freiburg, 2-8 July, 1978 / Ragnar Thoren (1978)

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