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The influence of web maps and education on adolescents’ global-scale cognitive map / Lieselot Lapon in Cartographic journal (the), Vol 57 n° 3 (August 2020)  

Public [article]  inCartographic journal (the) > Vol 57 n° 3 (August 2020) . - pp 221 - 234 Titre : The influence of web maps and education on adolescents’ global-scale cognitive map Type de document : Article/Communication Auteurs : Lieselot Lapon, Auteur ; Philippe De Maeyer, Auteur ; Bart De Wit, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 221 - 234 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] acquisition de connaissances [Termes descripteurs IGN] carte cognitive [Termes descripteurs IGN] cartographie par internet [Termes descripteurs IGN] déformation de projection [Termes descripteurs IGN] éducation [Termes descripteurs IGN] lecture de carte [Termes descripteurs IGN] projection [Termes descripteurs IGN] projection de Robinson [Termes descripteurs IGN] projection Universal Transverse Mercator [Termes descripteurs IGN] web mapping [Vedettes matières IGN] Cartologie Résumé : (auteur) Several factors influence the global-scale cognitive map. The use of school books, atlases and web maps all play an essential role in the development of geographical knowledge of adolescents. This research examines the impact of the educational system versus web maps on the adolescents’ mental map. Through a specially designed web application, university students and secondary school pupils estimated the real proportion of countries and continents compared to Europe. Participants with a more theoretical background or wider knowledge about map projections and its distortions estimated the real proportions more accurately. This research also found that the Robinson projection, commonly used in schoolbooks and atlases, is the best-known map projection among adolescents. However, the influence of web maps could not be proven since no Mercator effect was found. Education is of undeniable importance, and therefore, educational materials that encourage people to look more carefully and critically at maps should be further developed. Numéro de notice : A2020-804 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00087041.2019.1660512 date de publication en ligne : 02/03/2020 En ligne : https://doi.org/10.1080/00087041.2019.1660512 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96762 [article]

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Knowledge graphs and big data processing / Valentina Janev (2020)  

Public Titre : Knowledge graphs and big data processing Type de document : Monographie Auteurs : Valentina Janev, Editeur scientifique ; Damien Graux, Editeur scientifique ; Hajira Jabeen, Editeur scientifique ; Emanuel Sallinger, Editeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2020 Importance : 307 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-030-53199-7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information [Termes descripteurs IGN] analyse sémantique [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] données massives [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] énergie [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] graphe [Termes descripteurs IGN] ingénierie des connaissances [Termes descripteurs IGN] intelligence artificielle [Termes descripteurs IGN] système de gestion de connaissances Résumé : (éditeur) This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required. Note de contenu : 1- Ecosystem of big data 2- Knowledge graphs: The layered perspective 3- Big data outlook, tools, and architectures 4- Creation of knowledge graphs 5- Federated query processing 6- Reasoning in knowledge graphs: An embeddings spotlight 7- Scalable knowledge graph processing using SANSA 8- Context-based entity matching for big data 9- Survey on big data applications 10- Case study from the energy domain 11- Numéro de notice : 25928 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Monographie DOI : 10.1007/978-3-030-53199-7 En ligne : https://doi.org/10.1007/978-3-030-53199-7 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96189

Representation learning for natural language processing / Zhiyuan Liu (2020)  

Public contenu dans Directory of Open Access Books DOAB / Directory of Open Access Books DOAB (2012)   Titre : Representation learning for natural language processing Type de document : Monographie Auteurs : Zhiyuan Liu, Editeur scientifique ; Yankai Lin, Editeur scientifique ; Maosong Sun, Editeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2020 Importance : 334 p. ISBN/ISSN/EAN : 978-981-1555732-- Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] données massives [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] représentation des connaissances [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] traitement du langage naturel Résumé : (Editeur) This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. Note de contenu : 1. Representation Learning and NLP 1.1 Motivation 1.2 Why Representation Learning Is Important for NLP 1.3 Basic Ideas of Representation Learning 1.4 Development of Representation Learning for NLP 1.5 Learning Approaches to Representation Learning for NLP 1.6 Applications of Representation Learning for NLP 1.7 The Organization of This Book 2. Word Representation 2.1 Introduction 2.2 One-Hot Word Representation 2.3 Distributed Word Representation 2.4 Contextualized Word Representation 2.5 Extensions 2.6 Evaluation 3. Compositional Semantics 3.1 Introduction 3.2 Semantic Space 3.3 Binary Composition 3.4 N-Ary Composition 4. Sentence Representation 4.1 Introduction 4.2 One-Hot Sentence Representation 4.3 Probabilistic Language Model 4.4 Neural Language Model 4.5 Applications 5. Document Representation 5.1 Introduction 5.2 One-Hot Document Representation 5.3 Topic Model 5.4 Distributed Document Representation 5.5 Applications 6. Sememe Knowledge Representation 6.1 Introduction 6.2 Sememe Knowledge Representation 6.3 Applications 7. World Knowledge Representation 7.1 Introduction 7.2 Knowledge Graph Representation 7.3 Multisource Knowledge Graph Representation 7.4 Applications 8. Network Representation 8.1 Introduction 8.2 Network Representation 8.3 Graph Neural Networks 9. Cross-Modal Representation 9.1 Introduction 9.2 Cross-Modal Representation 9.3 Image Captioning 9.4 Visual Relationship Detection 9.5 Visual Question Answering 10. Resources 10.1 Open-Source Frameworks for Deep Learning 10.2 Open Resources for Word Representation 10.3 Open Resources for Knowledge Graph Representation 10.4 Open Resources for Network Representation 10.5 Open Resources for Relation Extraction 11. Outlook Numéro de notice : 26515 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.1007/978-981-15-5573-2 En ligne : http://doi.org/10.1007/978-981-15-5573-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97296

VGI contributors’ awareness of geographic information quality and its effect on data quality: a case study from Japan / Jun Yamashita in International journal of cartography, vol 5 n° 2-3 (July - November 2019)  

Public [article]  inInternational journal of cartography > vol 5 n° 2-3 (July - November 2019) . - pp 214 - 224 Titre : VGI contributors’ awareness of geographic information quality and its effect on data quality: a case study from Japan Type de document : Article/Communication Auteurs : Jun Yamashita, Auteur ; Toshikazu Seto, Auteur ; Yuichiro Nishimura, Auteur ; Nobusuke Iwasaki, Auteur Année de publication : 2019 Conférence : ICC 2019, 29th International Cartographic Conference ICA, Mapping everything for everyone 15/07/2019 20/07/2019 Tokyo Japon Article en page(s) : pp 214 - 224 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] cartographie collaborative [Termes descripteurs IGN] contributeur [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] fiabilité des données [Termes descripteurs IGN] Japon [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] précision des données [Termes descripteurs IGN] production participative [Termes descripteurs IGN] qualité des connaissances Résumé : (Auteur) In many countries, geospatial data are typically provided by public institutions. Cities have been mapped using such public data. On the other hand, the demand for geospatial data has been diversifying, given the requirements for mapping cities. To respond to demands for new geospatial data, creation of citizen-generated open data and volunteered geographic information (VGI) have recently become popular. However, the quality of such open data and VGI are not always guaranteed. The number of studies on quality assessments of VGI have increased in recent years. The present study aimed to identify OpenStreetMap (OSM), one type of VGI, as well as contributors’ awareness of data quality, and the relationships between their awareness and the positional accuracy of the OSM data contributed by them. The results showed that awareness or lack of the positional accuracy did not affect the quality of the OSM data created by the contributors. These findings suggest that the crowdsourcing approach might not guarantee the data quality of VGI. Numéro de notice : A2019-238 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2019.1613086 date de publication en ligne : 17/05/2019 En ligne : https://doi.org/10.1080/23729333.2019.1613086 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92938 [article]

Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM) / Wenzhi Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 237 - 250 Titre : Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM) Type de document : Article/Communication Auteurs : Wenzhi Zhao, Auteur ; Yanchen Bo, Auteur ; Jiage Chen, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 237 - 250 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classe sémantique [Termes descripteurs IGN] compréhension de l'image [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] reconnaissance d'objets [Termes descripteurs IGN] scène urbaine Résumé : (Auteur) Urban scenes refer to city blocks which are basic units of megacities, they play an important role in citizens’ welfare and city management. Remote sensing imagery with largescale coverage and accurate target descriptions, has been regarded as an ideal solution for monitoring the urban environment. However, due to the heterogeneity of remote sensing images, it is difficult to access their geographical content at the object level, let alone understanding urban scenes at the block level. Recently, deep learning-based strategies have been applied to interpret urban scenes with remarkable accuracies. However, the deep neural networks require a substantial number of training samples which are hard to satisfy, especially for high-resolution images. Meanwhile, the crowed-sourced Open Street Map (OSM) data provides rich annotation information about the urban targets but may encounter the problem of insufficient sampling (limited by the places where people can go). As a result, the combination of OSM and remote sensing images for efficient urban scene recognition is urgently needed. In this paper, we present a novel strategy to transfer existing OSM data to high-resolution images for semantic element determination and urban scene understanding. To be specific, the object-based convolutional neural network (OCNN) can be utilized for geographical object detection by feeding it rich semantic elements derived from OSM data. Then, geographical objects are further delineated into their functional labels by integrating points of interest (POIs), which contain rich semantic terms, such as commercial or educational labels. Lastly, the categories of urban scenes are easily acquired from the semantic objects inside. Experimental results indicate that the proposed method has an ability to classify complex urban scenes. The classification accuracies of the Beijing dataset are as high as 91% at the object-level and 88% at the scene level. Additionally, we are probably the first to investigate the object level semantic mapping by incorporating high-resolution images and OSM data of urban areas. Consequently, the method presented is effective in delineating urban scenes that could further boost urban environment monitoring and planning with high-resolution images. Numéro de notice : A2019-209 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.03.019 date de publication en ligne : 29/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.03.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92675 [article]

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GeoTxt: A scalable geoparsing system for unstructured text geolocation / Morteza Karimzadeh in Transactions in GIS, vol 23 n° 1 (February 2019)  

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An approach to measuring semantic relatedness of geographic terminologies using a thesaurus and lexical database sources / Zugang Chen in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)  

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Les observatoires territoriaux : Des outils de la société de la connaissance ? / Jean Philippe Tonneau in Revue internationale de géomatique, vol 27 n° 3 (juillet-septembre 2017)  

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Vers l'automatisation de la production de cartes / Guillaume Touya (2017)  

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Usability of an opportunistic interface concept for ad hoc ride-sharing / Michael Rigby in International journal of cartography, vol 2 n° 2 (December 2016)  

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Réduction du nombre des prédicats pour les approches de répartition des entrepôts de données / Mourad Ghorbel in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 21 n° 1 (janvier - février 2016)  

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Knowledge elicitation: methods, tools and techniques / Nigel R. Shadbolt (2015) 

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Crowdsourcing as a tool for knowledge acquisition in spatial planning / Chrysaida-Aliki Papadopoulou in Future internet, vol 6 n° 1 (March 2014)  

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Shared earth modeling / Michel Perrin (2013)

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Semi-automatic quality control of topographic data sets / Petra Helmholz in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 9 (September 2012)  

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