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Automated delineation of wildfire areas using Sentinel-2 satellite imagery / Mira Weirather in GI Forum, vol 2018 n° 1 ([01/01/2018])
[article]
Titre : Automated delineation of wildfire areas using Sentinel-2 satellite imagery Type de document : Article/Communication Auteurs : Mira Weirather, Auteur ; Gunter Zeug, Auteur ; Thomas Schneider, Auteur Année de publication : 2018 Article en page(s) : pp 251 - 262 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Copernicus (programme européen)
[Termes IGN] extraction automatique
[Termes IGN] image Sentinel-MSI
[Termes IGN] incendie de forêt
[Termes IGN] informatique en nuageRésumé : (auteur) Climate change will bring many changes to the world. For example, the frequency and severity of natural hazards and related disasters are expected to increase globally. Wildfires already affect thousands of people every year and cause billions of Euros’ worth of damage. It is therefore paramount to develop measures that help deal with the consequences of wildfires. Forests being the largest terrestrial ecosystem in the European Union and providing many ecosystem services, their loss due to wildfires is of serious concern. In this study, an algorithm to extract the burned area of wildfire events is presented. It was developed on the basis of three fire events in 2017. The procedure is fully automated, from downloading suitable data to determining the burned area by applying the differenced Normalized Burn Ratio (dNBR) on open Sentinel-2 satellite imagery from the European Copernicus programme. First results show good performance and encourage its further development and application. It is planned that the output of our mapping will feed into and be used in calibrating wildfire simulations during longer fire events. Numéro de notice : A2018-302 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1553/giscience2018_01_s251 En ligne : https://doi.org/10.1553/giscience2018_01_s251 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90400
in GI Forum > vol 2018 n° 1 [01/01/2018] . - pp 251 - 262[article]
Titre : Machine learning - advanced techniques and emerging applications Type de document : Monographie Auteurs : Hamed Farhadi, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2018 Importance : 230 p. Format : 19 x 27 cm ISBN/ISSN/EAN : 9781789237528 9781789237535 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] données massives
[Termes IGN] informatique en nuage
[Termes IGN] processeur graphique
[Termes IGN] statistiquesRésumé : (éditeur) The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses. Note de contenu : 1- Hardware accelerator design for machine learning
2- Regression models to predict air pollution from affordable data collections
3- Multiple kernel-based multimedia fusion for automated event detection from tweets
4- Using sentiment analysis and machine learning algorithms to determine citizens’ perceptions
5- Overcoming challenges in predictive modeling of Laser-plasma interaction scenarios. The sinuous route from advanced machine learning to deep learning
6- Machine learning approaches for spectrum management in cognitive radio networks
7- Machine learning algorithm for wireless indoor localization
8- classification of malaria-infected cells using deep convolutional neuronal networks
9- Machine learning in educational technology
10- Sentiment-based semantic rule learning for improved product recommandations
11- A multilevel evolutionary algorithm applied to the maximum satisfiability problemsNuméro de notice : 25952 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.69783 En ligne : https://doi.org/10.5772/intechopen.69783 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96406 A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data / Qunying Huang in Computers, Environment and Urban Systems, vol 66 (November 2017)
[article]
Titre : A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data Type de document : Article/Communication Auteurs : Qunying Huang, Auteur ; Guido Cervone, Auteur ; Guiming Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 23 - 37 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] caractérisation
[Termes IGN] catastrophe naturelle
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] exploration de données géographiques
[Termes IGN] exploration de texte
[Termes IGN] image numérique
[Termes IGN] informatique en nuage
[Termes IGN] inondation
[Termes IGN] intégration de données
[Termes IGN] interface web
[Termes IGN] prototype
[Termes IGN] tempête
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Social media streams and remote sensing data have emerged as new sources for tracking disaster events, and assessing their damages. Previous studies focus on a case-by-case approach, where a specific event was first chosen and filtering criteria (e.g., keywords, spatiotemporal information) are manually designed and used to retrieve relevant data for disaster analysis. This paper presents a framework that synthesizes multi-sourced data (e.g., social media, remote sensing, Wikipedia, and Web), spatial data mining and text mining technologies to build an architecturally resilient and elastic solution to support disaster analysis of historical and future events. Within the proposed framework, Wikipedia is used as a primary source of different historical disaster events, which are extracted to build an event database. Such a database characterizes the salient spatiotemporal patterns and characteristics of each type of disaster. Additionally, it can provide basic semantics, such as event name (e.g., Hurricane Sandy) and type (e.g., flooding) and spatiotemporal scopes, which are then tuned by the proposed procedures to extract additional information (e.g., hashtags for searching tweets), to query and retrieve relevant social media and remote sensing data for a specific disaster. Besides historical event analysis and pattern mining, the cloud-based framework can also support real-time event tracking and monitoring by providing on-demand and elastic computing power and storage capabilities. A prototype is implemented and tested with data relative to the 2011 Hurricane Sandy and the 2013 Colorado flooding. Numéro de notice : a2017-430 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2017.06.004 En ligne : https://doi.org/10.1016/j.compenvurbsys.2017.06.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86330
in Computers, Environment and Urban Systems > vol 66 (November 2017) . - pp 23 - 37[article]Culture 3D cloud: A cloud computing platform for 3D scanning, documentation, preservation and dissemination of cultural heritage / Pierre Alliez in ERCIM News, n° 111 (October 2017)
[article]
Titre : Culture 3D cloud: A cloud computing platform for 3D scanning, documentation, preservation and dissemination of cultural heritage Type de document : Article/Communication Auteurs : Pierre Alliez, Auteur ; François Forge, Auteur ; Livio de Luca, Auteur ; Marc Pierrot-Deseilligny , Auteur ; Marius Preda, Auteur Année de publication : 2017 Article en page(s) : pp 35 - 35 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] base de données localisées 3D
[Termes IGN] informatique en nuage
[Termes IGN] patrimoine culturel
[Termes IGN] patrimoine immobilier
[Termes IGN] plateforme logicielleRésumé : (auteur) One of the limitations of the 3D digitisation process is that it typically requires highly specialised skills andyields heterogeneous results depending on proprietary software solutions and trial-and-error practices. Themain objective of Culture 3D Cloud [L1], a collaborative project funded within the framework of the French“Investissements d’Avenir” programme, is to overcome this limitation, providing the cultural community witha novel image-based modelling service for 3D digitisation of cultural artefacts. This will be achieved byleveraging the widespread expert knowledge of digital photography in the cultural arena to enable culturalheritage practitioners to perform routine 3D digitisation via photo-modelling. Cloud computing was chosenfor its capability to offer high computing resources at reasonable cost, scalable storage via continuouslygrowing virtual containers, multi-support diffusion via remote rendering and efficient deployment of releases. Numéro de notice : A2017-680 Affiliation des auteurs : LASTIG LOEMI+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97385
in ERCIM News > n° 111 (October 2017) . - pp 35 - 35[article]Documents numériques
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Culture 3D Cloud - pdf éditeurAdobe Acrobat PDF VGI in surveying engineering : Introducing collaborative cloud land surveying / Ioannis Sofos in Journal of Spatial Information Science (JoSIS), n° 15 (September 2017)
[article]
Titre : VGI in surveying engineering : Introducing collaborative cloud land surveying Type de document : Article/Communication Auteurs : Ioannis Sofos, Auteur ; Vassilios Vescoukis, Auteur ; Maria Tsakiri-Strati, Auteur Année de publication : 2017 Article en page(s) : pp 35 - 64 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] Athènes
[Termes IGN] données localisées des bénévoles
[Termes IGN] entrepôt de données localisées
[Termes IGN] géomètre
[Termes IGN] informatique en nuage
[Termes IGN] partage de données localisées
[Termes IGN] topographeRésumé : (Auteur) Volunteered geographic information (VGI) has enabled many innovative applications in various scientific fields. This paper introduces a new framework called “collaborative cloud-based land surveying” (CCLS) that uses VGI principles for data sharing among surveyor engineers to boost the productivity and improve the quality of their applications. A cloud-based spatio-temporal data repository is presented, aiming to facilitate the sharing of VGI among surveyor engineers. A fully-functional distributed software application has been developed and used to apply CCLS in a large-scale land surveying project run by the Greek Ministry of Culture, which involves the mapping of the historic center of Athens. Results from the data analysis of hundreds of measurements indicate a substantial (30% to 60%) error reduction and also a significant productivity raise (22%). The collected measurements are shared in an online database, accessible by professional surveyors who can in turn contribute their own data to further enhance the CCLS system. Numéro de notice : A2017-821 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5311/JOSIS.2017.15.320 En ligne : https://doi.org/10.5311/JOSIS.2017.15.320 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89306
in Journal of Spatial Information Science (JoSIS) > n° 15 (September 2017) . - pp 35 - 64[article]A cyber-enabled spatial decision support system to inventory mangroves in Mozambique: coupling scientific workflows and cloud computing / Wenwu Tang in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)PermalinkDeveloping an integrated cloud-based spatial-temporal system for monitoring phenology / M. Cope in Ecological Informatics, vol 39 (May 2017)PermalinkEnabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley’s K function / Guiming Zhang in International journal of geographical information science IJGIS, vol 30 n° 11-12 (November - December 2016)PermalinkService-oriented model-encapsulation strategy for sharing and integrating heterogeneous geo-analysis models in an open web environment / Songshan Yue in ISPRS Journal of photogrammetry and remote sensing, vol 114 (April 2016)PermalinkPermalinkForming a global monitoring mechanism and a spatiotemporal performance model for geospatial services / Jizhe Xia in International journal of geographical information science IJGIS, vol 29 n° 3 (March 2015)PermalinkArchitecture de l'information / Jean-Michel Salaün (2015)PermalinkQuand le Cloud favorise de nouveaux modes de commercialisation / Françoise de Blomac in DécryptaGéo le mag, n° 163 (janvier 2015)PermalinkCartographie web et mobile / Michael P. Peterson in Cartes & Géomatique, n° 221 (septembre 2014)PermalinkGeospatial collaboration in the cloud / Patrick Collins in GEO: Geoconnexion international, vol 13 n° 7 (July 2014)Permalink