Centre de documentation
scientifique

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

Investigating the role of wind disturbance in tropical forests through a forest dynamics model and satellite observations / E-Ping Rau (2022)  

Public Titre : Investigating the role of wind disturbance in tropical forests through a forest dynamics model and satellite observations Type de document : Thèse/HDR Auteurs : E-Ping Rau, Auteur ; Jérôme Chave, Directeur de thèse Editeur : Toulouse : Université de Toulouse 3 Paul Sabatier Année de publication : 2022 Importance : 184 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse 3 Paul Sabatier Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse forestière [Termes IGN] canopée [Termes IGN] chablis (sylviculture) [Termes IGN] cyclone [Termes IGN] forêt tropicale [Termes IGN] Guyane française [Termes IGN] image Sentinel-SAR [Termes IGN] modèle dynamique [Termes IGN] perturbation écologique [Termes IGN] précipitation [Termes IGN] risque naturel [Termes IGN] sécheresse [Termes IGN] traitement d'image radar [Termes IGN] vent Index. décimale : THESE Thèses et HDR Résumé : (Auteur) Natural disturbances have an important influence on the structure, composition and functioning of tropical forests and a role in the regulation of biogeochemical cycles. The frequency and intensity of natural disturbances are modified by climate change: a better knowledge of their mechanism of action is necessary to predict the consequences of this modification. Modeling allows us to evaluate the role of each of the ecological processes and their link with environmental factors. Remote sensing tools inform us about the structure and functioning of forests at large scales, and can be useful for the calibration and validation of vegetation models. In this thesis, I employed both approaches to examine how tropical forests are shaped by natural disturbances, particularly wind, which is a major disturbance factor in many tropical regions. First, I evaluated the transferability of a spatially explicit, individual-based model via sensitivity testing and calibration of global parameters. The model correctly predicts forest structure at two contrasting sites, and its response is consistent with variations in climate forcing. Calibration of a small number of key parameters was required, including the parameter controlling mortality and crown allometry. To investigate the sensitivity of the model to mortality, I implemented a wind damage module based on biophysical principles and coupled with wind speed to model forest responses to extreme wind events. With increasing disturbance level, canopy height decreased steadily but biomass showed a non-linear response. Wind intensity had a strong impact on canopy height and biomass, but not the frequency of extreme wind events. Finally, I tested whether radar data from Sentinel-1 satellites could be used to detect gaps due to natural disturbances in French Guiana. The Sentinel-1 data detected more natural gaps above 0.2 ha than the optical satellite data, and they showed a spatial pattern consistent with the optical images. The level of disturbance did not vary with altitude. We found more disturbance during dry seasons, which could be due to the delayed response of precipitation rather than the direct response of drought. In conclusion, this thesis demonstrates that the integration between modeling and remote sensing sheds light on the effects of natural disturbances on tropical forests. The resulting results can be used to study other types of disturbances and their interactions on a large scale. Note de contenu : General introduction General methods 1: Transferability of an individual- and trait-based forest dynamics model: a test case across the tropics 1.1 Abstract 1.2 Introduction 1.3 Materials and methods 1.4 Results 1.5 Discussion 1.6 Acknowledgements and author contributions 1.7 Supplementary data 2: Wind speed controls forest structure in subtropical forests exposed to cyclones: a case study using an individual-based model 2.1 Abstract 2.2 Introduction 2.3 Material and methods 2.4 Results 2.5 Discussion 2.6 Acknowledgments and author contributions 2.7 Supplementary data 3: Detecting Natural Disturbances in Tropical Forests Using Sentinel-1 SAR Data: a Test in French Guiana 3.1 Abstract 3.2 Introduction 3.3 Methods 3.4 Results 3.5 Discussions 3.6 Acknowledgments and author contributions 3.7 Supplementary data General discussion and conclusions Numéro de notice : 26836 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Ecologie, biodiversité et évolution : Toulouse 3 : 2022 nature-HAL : Thèse DOI : sans Date de publication en ligne : 20/06/2022 En ligne : https://tel.hal.science/tel-03699667 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101075

A topic model based framework for identifying the distribution of demand for relief supplies using social media data / Ting Zhang in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2216 - 2237 Titre : A topic model based framework for identifying the distribution of demand for relief supplies using social media data Type de document : Article/Communication Auteurs : Ting Zhang, Auteur ; Shi Shen, Auteur ; Changxiu Cheng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2216 - 2237 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] allocation de Dirichlet latente [Termes IGN] cartographie thématique [Termes IGN] catastrophe naturelle [Termes IGN] cyclone [Termes IGN] distribution spatiale [Termes IGN] données issues des réseaux sociaux [Termes IGN] Pacifique ouest [Termes IGN] Philippines [Termes IGN] répertoire toponymique [Termes IGN] secours d'urgence Résumé : (auteur) Natural disasters have caused substantial economic losses and numerous casualties. The demand analysis of relief supplies is the premise and basis for efficient relief operations after disasters. With the widespread use of social media, it has become a vital channel for people to report their demand for relief supplies and provides a way to obtain information on disaster areas. Therefore, we present a topic model-based framework and establish a demand dictionary and a gazetteer that aims to identify the spatial distribution of the demand for relief supplies by using social media data. Taking the 2013 Typhoon Haiyan (also called Yolanda) as a case study, we identify the potential topics of tweets with the biterm topic model, screen the tweets related to demands, and obtain the demand and location information from tweets to study the distribution of the relief supplies needs. The results show that, based on the demand dictionary, a gazetteer and the biterm topic model, the effective demand for relief supplies can be extracted from tweets. The proposed framework is feasible for the identification of accurate demand information and its distribution. Further, this framework can be applied to other types of disaster responses and can facilitate relief operations. Numéro de notice : A2021-757 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1869746 Date de publication en ligne : 07/01/2021 En ligne : https://doi.org/10.1080/13658816.2020.1869746 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98772 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2021111	SL	Revue	Centre de documentation	Revues en salle	Disponible

GNSS-based statistical analysis of ionospheric anomalies during typhoon landings in Taiwan/Japan / Hai Peng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp 5272 - 5279 Titre : GNSS-based statistical analysis of ionospheric anomalies during typhoon landings in Taiwan/Japan Type de document : Article/Communication Auteurs : Hai Peng, Auteur ; Yibin Yao, Auteur ; Jian Kong, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 5272 - 5279 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] cyclone [Termes IGN] données GNSS [Termes IGN] onde de gravité [Termes IGN] perturbation ionosphérique [Termes IGN] phase [Termes IGN] propagation ionosphérique [Termes IGN] signal GNSS [Termes IGN] Taïwan [Termes IGN] teneur totale en électrons [Termes IGN] vitesse Résumé : (auteur) Using the Global Navigation Satellite System (GNSS) differenced total electron content (dTEC) series, the traveling ionosphere disturbances (TIDs) of 22 typhoons registered in Taiwan/Japan between 2013 and 2016 were studied. The horizontal speed of the first TID during a typhoon landing can be estimated by a two-station method with the ionosphere anomaly indicator in total electron count units (TECUs) (|dTEC| ≥ 0.15 TECU). The horizontal speed of the TIDs was from 155 to 210 m/s and with an average speed of 168.70 m/s. The estimated TID speeds of Typhoons Soudelor (205.93 m/s) and Megi (158.47 m/s) are not consistent with each other, even though they had very similar trajectories when cross through Taiwan Island. Moreover, the propagation velocity of the typhoon ionospheric anomaly showed a significant positive correlation ( r=0.78 , α=0.05 ) with the change rate of the typhoon central air pressure and a negative correlation ( r=−0.52 , α=0.05 ) with the central pressure before landing. Gravity waves were generated by land friction, terrain blocking, and strong wind shear transport energy into the atmosphere from the near surface to the mesosphere and thermosphere, which is the main cause of ionosphere disturbances during typhoon landing. Numéro de notice : A2021-428 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3004829 Date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3004829 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97784 [article]

Learning from multimodal and multitemporal earth observation data for building damage mapping / Bruno Adriano in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 132 - 143 Titre : Learning from multimodal and multitemporal earth observation data for building damage mapping Type de document : Article/Communication Auteurs : Bruno Adriano, Auteur ; Naoto Yokoya, Auteur ; Junshi Xia, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 132 - 143 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] cartographie des risques [Termes IGN] catastrophe naturelle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] cyclone [Termes IGN] dommage [Termes IGN] données multitemporelles [Termes IGN] image à haute résolution [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] observation de la Terre [Termes IGN] segmentation sémantique [Termes IGN] séisme [Termes IGN] surveillance d'ouvrage [Termes IGN] tsunami Résumé : (auteur) Earth observation (EO) technologies, such as optical imaging and synthetic aperture radar (SAR), provide excellent means to continuously monitor ever-growing urban environments. Notably, in the case of large-scale disasters (e.g., tsunamis and earthquakes), in which a response is highly time-critical, images from both data modalities can complement each other to accurately convey the full damage condition in the disaster aftermath. However, due to several factors, such as weather and satellite coverage, which data modality will be the first available for rapid disaster response efforts is often uncertain. Hence, novel methodologies that can utilize all accessible EO datasets are essential for disaster management. In this study, we developed a global multimodal and multitemporal dataset for building damage mapping. We included building damage characteristics from three disaster types, namely, earthquakes, tsunamis, and typhoons, and considered three building damage categories. The global dataset contains high-resolution (HR) optical imagery and high-to-moderate-resolution SAR data acquired before and after each disaster. Using this comprehensive dataset, we analyzed five data modality scenarios for damage mapping: single-mode (optical and SAR datasets), cross-modal (pre-disaster optical and post-disaster SAR datasets), and mode fusion scenarios. We defined a damage mapping framework for semantic segmentation of damaged buildings based on a deep convolutional neural network (CNN) algorithm. We also compared our approach to another state-of-the-art model for damage mapping. The results indicated that our dataset, together with a deep learning network, enabled acceptable predictions for all the data modality scenarios. We also found that the results from cross-modal mapping were comparable to the results obtained from a fusion sensor and optical mode analysis. Numéro de notice : A2021-272 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.016 Date de publication en ligne : 17/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.016 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97343 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2021051	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2021052	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2021053	DEP-RECP	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

Dynamics of inundation events in the rivers-estuaries-ocean continuum in Bengal delta : synergy between hydrodynamic modelling and spaceborne remote sensing / Md Jamal Uddin Kahn (2021)  

Permalink

Optimisations cartographiques pour la gestion des crises et des risques majeurs : le cas de la cartographie des dommages post-catastrophes / Thomas Candela (2021)  

Permalink

Impact of INSAT-3D/3DR radiance data assimilation in predicting tropical cyclone Titli over the bay of Bengal / Raghu Nadimpalli in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)  

Permalink

Impact of extreme weather events on urban human flow: A perspective from location-based service data / Zhenhua Chen in Computers, Environment and Urban Systems, vol 83 (September 2020)  

Permalink

Long time-series remote sensing analysis of the periodic cycle evolution of the inlets and ebb-tidal delta of Xincun Lagoon, Hainan Island, China / Huaguo Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)  

Permalink

Étude de la vapeur d’eau atmosphérique à partir de données GNSS dans le bassin sud-ouest de l’océan Indien et applications à l’étude du climat et des cyclones tropicaux / Edouard Lees (2020)  

Permalink

Remote sensing of precipitation, 1. Volume 1 / Silas Michaelides (2019)  

Permalink

Remote sensing of precipitation, 2. Volume 2 / Silas Michaelides (2019)  

Permalink

Understanding of atmospheric systems with efficient numerical methods for observation and prediction / Lei-Ming Ma (2019)  

Permalink

Association rules-based multivariate analysis and visualization of spatiotemporal climate data / Feng Wang in ISPRS International journal of geo-information, vol 7 n° 7 (July 2018)  

Permalink