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A novel computational knowledge-base framework for visualization and quantification of geospatial metadata in spatial data infrastructures / Gangothri Rajaram in Geoinformatica, vol 22 n° 2 (April 2018)
[article]
Titre : A novel computational knowledge-base framework for visualization and quantification of geospatial metadata in spatial data infrastructures Type de document : Article/Communication Auteurs : Gangothri Rajaram, Auteur ; Harish Chandra Karnatak, Auteur ; Swaminathan Venkatraman, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 269 - 305 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] cadre conceptuel
[Termes IGN] métadonnées
[Termes IGN] métadonnées géographiques
[Termes IGN] pondération
[Termes IGN] qualité des metadonnées
[Termes IGN] service webRésumé : (Auteur) Advances in Metadata research have been instrumental in predictions and `fitness-of-use evaluation' for the effective Decision-making process. For the past two decades, the model has been developed to provide visual assistance for assessing the quality information in metadata and quantifying the degree of metadata population. Still, there is a need to develop a framework that can be generic to adopt all the standards available for Geospatial Metadata. The computational analysis of metadata for specific applications remains uncharted for investigations and studies. This work proposes a computational framework for Geospatial Metadata by integrating TopicMaps and Hypergraphs (HXTM) based on the elements and their dependency relationships. A purpose-built dataset extracted from schemas of various standardisation organisations and existing knowledge in the discipline is utilised to model the framework and thereby evaluate ranking strategies. Hypergraph-Helly Property based Weight-Assignment Algorithm (HHWA) have been proposed for HXTM framework to calculate Stable weights for Metadata Elements. Recursive use of Helly-property ensures predominant elements, while Rank Order Centroid (ROC) method is used to compute standard weights. A real corpus using case studies from FGDC's Standard for Geospatial Metadata, INSPIRE Metadata Standards, and ISRO Metadata Content Standard (NSDI 2.0) is used to validate the proposed framework. The observations show that the Information Gain (Entropy) of the proposed model along with the algorithm proves to be computationally smart for quantification purposes and visualises the strength of Metadata Elements for all applications. A prototype tool, `MetDEVViz- MetaData Editor, Validator &Visualization' is designed to exploit the benefits of the proposed algorithm for the case studies that acts as a web service to provide a user interface for editing, validating and visualizing metadata elements. Numéro de notice : A2018-365 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-018-0317-6 Date de publication en ligne : 28/02/2018 En ligne : https://doi.org/10.1007/s10707-018-0317-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90730
in Geoinformatica > vol 22 n° 2 (April 2018) . - pp 269 - 305[article]Similarity measurement of metadata of geospatial data : an artificial neural network approach / Zugang Chen in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)
[article]
Titre : Similarity measurement of metadata of geospatial data : an artificial neural network approach Type de document : Article/Communication Auteurs : Zugang Chen, Auteur ; Jia Song, Auteur ; Yaping Yang, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] données localisées
[Termes IGN] métadonnées
[Termes IGN] métadonnées géographiques
[Termes IGN] réseau neuronal artificiel
[Termes IGN] similitudeRésumé : (Auteur) To help users discover the most relevant spatial datasets in the ever-growing global spatial data infrastructures (SDIs), a number of similarity measures of geospatial data based on metadata have been proposed. Researchers have assessed the similarity of geospatial data according to one or more characteristics of the geospatial data. They created different similarity algorithms for each of the selected characteristics and then combined these elementary similarities to the overall similarity of the geospatial data. The existing combination methods are mainly linear and may not be the most accurate. This paper reports our experiences in attempting to learn the optimal non-linear similarity integration functions, from the knowledge of experts, using an artificial neural network. First, a multiple-layer feed forward neural network (MLFFN) was created. Then, the intrinsic characteristics were used to represent the metadata of geospatial data and the similarity algorithms for each of the intrinsic characteristics were built. The training and evaluation data of MLFFN were derived from the knowledge of domain experts. Finally, the MLFFN was trained, evaluated, and compared with traditional linear combination methods, which was mainly a weighted sum. The results show that our method outperformed the existing methods in terms of precision. Moreover, we found that the combination of elementary similarities of experts to the overall similarity of geospatial data was not linear. Numéro de notice : A2018-094 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7030090 En ligne : https://doi.org/10.3390/ijgi7030090 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89506
in ISPRS International journal of geo-information > vol 7 n° 3 (March 2018)[article]Assessing the openness of Spatial Data Infrastructures (SDI) : towards a map of open SDI / Glenn Vancauwenberghe in International Journal of Spatial Data Infrastructures Research, vol 13 (Year 2018)
[article]
Titre : Assessing the openness of Spatial Data Infrastructures (SDI) : towards a map of open SDI Type de document : Article/Communication Auteurs : Glenn Vancauwenberghe, Auteur ; Kotryna Valečkaitė, Auteur ; Bastiaan Van Loenen, Auteur ; Frederika Welle Donker, Auteur Année de publication : 2018 Article en page(s) : pp 88 - 100 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] carte thématique
[Termes IGN] données ouvertesRésumé : (Auteur) This paper introduces the Open Spatial Data Infrastructure (SDI) Assessment Framework as a new approach for assessing the openness of SDIs. Open SDIs are SDIs in which non-government actors such as businesses, citizens, researchers and non-profit organizations can contribute to the development and implementation of the SDI, use spatial data with as few restrictions as possible and benefit from using these geographic data. A pilot application of the new framework resulted in the Map of Open SDI in Europe, which aims to show the level of openness of national SDIs in Europe. The map could become a relevant and practical tool that shows the status of Open SDIs in Europe and supports decision makers and practitioners in making their own SDI more open. Numéro de notice : A2018-641 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.2902/1725-0463.2018.13.art9 En ligne : http://dx.doi.org/10.2902/1725-0463.2018.13.art9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93304
in International Journal of Spatial Data Infrastructures Research > vol 13 (Year 2018) . - pp 88 - 100[article]
Titre : Les données géographiques souveraines : rapport au gouvernement Type de document : Rapport Auteurs : Valéria Faure-Muntian, Auteur Editeur : Paris : Ministère de la Transition écologique et solidaire MTES Année de publication : 2018 Importance : 68 p. Langues : Français (fre) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] données localisées souveraines
[Termes IGN] infrastructure française des données localiséesRésumé : Le rapport de madame la députée Valéria FAURE-MUNTIAN au gouvernement sur les données géographiques souveraines, contient des recommandations visant notamment à mieux fédérer les producteurs de données géographiques souveraines, à améliorer la coordination des acteurs des données géographiques souveraines, en faisant jouer au CNIG un rôle de coordination et d’animation de ces acteurs. Note de contenu : Introduction
Synthèse des recommandations
1. Définir la donnée géographique souveraine
2. Produire la donnée géographique souveraine
3. Propositions d'actions prioritaires
4. Financer la donnée géographique souveraine
ConclusionNuméro de notice : 17732 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Rapport officiel DOI : sans En ligne : https://www.vie-publique.fr/rapport/37550-les-donnees-geographiques-souveraines- [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100635
Titre : Earth observation open science and innovation Type de document : Monographie Auteurs : Pierre-Philippe Mathieu, Éditeur scientifique ; Christoph Aubrecht, Éditeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2018 Importance : 344 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-319-65633-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] analyse de données
[Termes IGN] données localisées
[Termes IGN] données localisées numériques
[Termes IGN] données massives
[Termes IGN] données spatiotemporelles
[Termes IGN] intelligence artificielle
[Termes IGN] internet des objets
[Termes IGN] observation de la Terre
[Termes IGN] science citoyenne
[Termes IGN] web des donnéesRésumé : (éditeur) Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites. This book reviews these megatrends and their implications for the Earth Observation community as well as the wider data economy. It provides insight into new paradigms of Open Science and Innovation applied to space data, which are characterized by openness, access to large volume of complex data, wide availability of new community tools, new techniques for big data analytics such as Artificial Intelligence, unprecedented level of computing power, and new types of collaboration among researchers, innovators, entrepreneurs and citizen scientists. In addition, this book aims to provide readers with some reflections on the future of Earth Observation, highlighting through a series of use cases not just the new opportunities created by the New Space revolution, but also the new challenges that must be addressed in order to make the most of the large volume of complex and diverse data delivered by the new generation of satellites. Note de contenu : 1- The Changing Landscape of Geospatial Information Markets
2- The Digital Transformation of Education
3- The Open Science Commons for the European Research Area
4- Citizen Science for Observing and Understanding the Earth
5- Fostering Cross-Disciplinary Earth Science Through Datacube Analytics
6- Mind the Gap: Big Data vs. Interoperability and Reproducibility of Science
7- Cyber-Infrastructure for Data-Intensive Geospatial Computing
8- Machine Learning Applications for Earth Observation
9- New Generation Platforms for Exploration of Crowdsourced Geo-Data
10- Mapping Land Use Dynamics Using the Collective Power of the Crowd
11- The Emergence of the GeoSharing Economy
12- Sustainable Agriculture and Smart Farming
13- Earth Observation Data for Enterprise Business Applications
14- Development of an Earth Observation Cloud Platform in Support to Water Resources Monitoring
15- Putting Big Data Innovation into Action for Development
16- Mapping Floods and Assessing Flood Vulnerability for Disaster Decision-Making: A Case Study Remote Sensing Application in Senegal
17- Earth Observation and Geospatial Implementation: Fueling Innovation in a Changing World
18- Artificial Intelligence and Earth Observation to Explore Water Quality in the Wadden Sea
19- Erratum to: Citizen Science for Observing and Understanding the EarthNuméro de notice : 25947 Affiliation des auteurs : non IGN Thématique : SOCIETE NUMERIQUE Nature : Monographie DOI : 10.1007/978-3-319-65633-5 En ligne : https://doi.org/10.1007/978-3-319-65633-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96356 Governance of national spatial data infrastructures in Europe / Joep Crompvoets in International Journal of Spatial Data Infrastructures Research, vol 13 (Year 2018)PermalinkGeospatial big data and archaeology: Prospects and problems too great to ignore / Mark D. McCoy in Journal of archaeological science, vol 84 (August 2017)PermalinkAggregation-based information retrieval system for geospatial data catalogs / Javier Lacasta in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)PermalinkAutomatic spatial metadata systems – the case of Australian urban research infrastructure network / Moshen Kalantari in Cartography and Geographic Information Science, Vol 44 n° 4 (July 2017)PermalinkInfrastructures de données géographiques et observatoires de recherche en environnement : Un exemple de mise en œuvre / Françoise Gourmelon in Revue internationale de géomatique, vol 27 n° 3 (juillet-septembre 2017)Permalinkvol 27 n° 3 - juillet-septembre 2017 - Observatoires environnementaux : innovations et dispositifs (Bulletin de Revue internationale de géomatique) / Thérèse LibourelPermalinkRapport du CNIG sur l'état d'Inspire en France / Anonyme in Géomatique expert, n° 116 (mai - juin 2017)PermalinkPermalinkL’information géographique et l’open data / Association française pour l'information géographique (2017)PermalinkA modelling framework for the study of Spatial Data Infrastructures applied to coastal management and planning / Jade Georis-Creuseveau in International journal of geographical information science IJGIS, vol 31 n° 1-2 (January - February 2017)Permalink