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A method to produce metadata describing and assessing the quality of spatial landmark datasets in mountain area / Marie-Dominique Van Damme (2022)  

Public Titre : A method to produce metadata describing and assessing the quality of spatial landmark datasets in mountain area Type de document : Article/Communication Auteurs : Marie-Dominique Van Damme , Auteur ; Ana-Maria Olteanu-Raimond , Auteur Editeur : Göttingen : Copernicus publications Année de publication : 2022 Projets : CHOUCAS / Olteanu-Raimond, Ana-Maria Conférence : AGILE 2022, 25th international AGILE Conference on Geographic Information Science, Artificial intelligence in the service of geospatial technologies 14/06/2022 17/06/2022 Vilnius Lithuanie OA Proceedings Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] Alpes (France) [Termes IGN] appariement de données localisées [Termes IGN] données localisées [Termes IGN] données ouvertes [Termes IGN] jeu de données localisées [Termes IGN] loisir [Termes IGN] métadonnées géographiques [Termes IGN] montagne [Termes IGN] norme ISO [Termes IGN] ontologie [Termes IGN] qualité des données Résumé : (auteur) The increase of recreational activities in the mountains and a growing amount of websites proposing geographic data, offer new opportunities for societal needs such as mountain rescue, biodiversity monitoring, outdoor activities. However, the main issue with the websites data is the lack of metadata that minimizes its reuse outside the community that produced the data. The goal of this paper is to study and generate quality and descriptive metadata using ISO standards. To this end, we propose a method based on a common vocabulary such as an ontology and a data matching process. The first one allows to associate to each type of feature from an available geographic dataset an ontology class that will facilitate data matching, reproducibility of results and minimize semantic heterogeneity. The second one allows to define matching links between features representing the same entity in the real world and compute quality indicators based on the validated links. Finally, at the end of this process, we are able to generate descriptive and quality metadata. By following ISO standards and using the QualityML dictionary for measures, the metadata is serialized to XML and can finally be published as open source. Our approach was applied to five different landmark datasets in the French Alps region. New insights were acquired regarding positional accuracy and semantic granularity. Numéro de notice : C2022-027 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/agile-giss-3-17-2022 Date de publication en ligne : 10/06/2022 En ligne : https://doi.org/10.5194/agile-giss-3-17-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100928

Consistency assessment for open geodata integration: an ontology-based approach / Linfang Ding in Geoinformatica, vol 25 n° 4 (October 2021)  

Public [article]  inGeoinformatica > vol 25 n° 4 (October 2021) . - pp 733 - 758 Titre : Consistency assessment for open geodata integration: an ontology-based approach Type de document : Article/Communication Auteurs : Linfang Ding, Auteur ; Guohui Xiao, Auteur ; Diego Calvanese, Auteur ; Liqiu Meng, Auteur Année de publication : 2021 Article en page(s) : pp 733 - 758 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] cadre conceptuel [Termes IGN] cohérence des données [Termes IGN] données hétérogènes [Termes IGN] données localisées [Termes IGN] données ouvertes [Termes IGN] intégration de données [Termes IGN] Italie [Termes IGN] ontologie Résumé : (auteur) Integrating heterogeneous geospatial data sources is important in various domains like smart cities, urban planning and governance, but remains a challenging research problem. In particular, the production of high-quality integrated data from multiple sources requires an understanding of their respective characteristics and a systematic assessment of the consistency within and between the data sources. In order to perform the assessment, the data has to be placed on a common ground. However, in practice, heterogeneous geodata are often provided in diverse formats and organized in significantly different structures. In this work, we propose a framework that uses an ontology-based approach to overcome the heterogeneity by means of a domain ontology, so that consistency rules can be evaluated at the unified ontological representation of the data sources. In our case study, we use open governmental data from Open Data Portals (ODPs) and volunteered geographic information from OpenStreetMap (OSM) as two test data sources in the area of the province of South Tyrol, Italy. Our preliminary experiment shows that the approach is effective in detecting inconsistencies within and between ODP and OSM data. These findings provide valuable insights for a better combined usage of these datasets. Numéro de notice : A2021-967 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-019-00384-9 Date de publication en ligne : 03/12/2019 En ligne : https://doi.org/10.1007/s10707-019-00384-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100388 [article]

Quality assessment of heterogeneous training data sets for classification of urban area with Landsat imagery / Neema Nicodemus Lyimo in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 5 (May 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 5 (May 2021) . - pp 339-348 Titre : Quality assessment of heterogeneous training data sets for classification of urban area with Landsat imagery Type de document : Article/Communication Auteurs : Neema Nicodemus Lyimo, Auteur ; Fang Luo, Auteur ; Qimin Cheng, Auteur ; Hao Peng, Auteur Année de publication : 2021 Article en page(s) : pp 339-348 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] appariement d'images [Termes IGN] distance euclidienne [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données hétérogènes [Termes IGN] données localisées des bénévoles [Termes IGN] données massives [Termes IGN] données ouvertes [Termes IGN] image Landsat [Termes IGN] incertitude des données [Termes IGN] jeu de données localisées [Termes IGN] qualité des données [Termes IGN] système à base de connaissances [Termes IGN] zone urbaine Résumé : (Auteur) Quality assessment of training samples collected from heterogeneous sources has received little attention in the existing literature. Inspired by Euclidean spectral distance metrics, this article derives three quality measures for modeling uncertainty in spectral information of open-source heterogeneous training samples for classification with Landsat imagery. We prepared eight test case data sets from volunteered geographic information and open government data sources to assess the proposed measures. The data sets have significant variations in quality, quantity, and data type. A correlation analysis verifies that the proposed measures can successfully rank the quality of heterogeneous training data sets prior to the image classification task. In this era of big data, pre-classification quality assessment measures empower research scientists to select suitable data sets for classification tasks from available open data sources. Research findings prove the versatility of the Euclidean spectral distance function to develop quality metrics for assessing open-source training data sets with varying characteristics for urban area classification. Numéro de notice : A2021-366 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.5.339 Date de publication en ligne : 01/05/2021 En ligne : https://doi.org/10.14358/PERS.87.5.339 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97695 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2021051	SL	Revue	Centre de documentation	Revues en salle	Disponible

Mining the semantic Web for OWL axioms / Thu Huong Nguyen (2021)  

Public Titre : Mining the semantic Web for OWL axioms Titre original : Fouille du Web sémantique à la recherche d'axiomes OWL Type de document : Thèse/HDR Auteurs : Thu Huong Nguyen, Auteur ; Andrea Tettamanzi, Directeur de thèse Editeur : Nice : Université Côte d'Azur Année de publication : 2021 Importance : 175 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat présentée en vue de l’obtention du grade de docteur en Informatique de l’Université Côte d’Azur Langues : Français (fre) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] algorithme génétique [Termes IGN] données ouvertes [Termes IGN] exploration de données [Termes IGN] logique floue [Termes IGN] ontologie [Termes IGN] OWL [Termes IGN] RDF [Termes IGN] théorie des possibilités [Termes IGN] web des données [Termes IGN] web sémantique Index. décimale : THESE Thèses et HDR Résumé : (auteur) In the Semantic Web era, Linked Open Data (LOD) is its most successful implementation, which currently contains billions of RDF (Resource Data Framework) triples derived from multiple, distributed, heterogeneous sources. The role of a general semantic schema, represented as an ontology, is essential to ensure the correctness and consistency in LOD and make it possible to infer implicit knowledge by reasoning. The growth of LOD creates an opportunity for the discovery of ontological knowledge from its raw RDF data itself to enrich relevant knowledge bases. In this work, we aim at discovering schema-level knowledge in the form of axioms encoded in OWL (Ontology Web Language) from RDF data. The approaches to automated generation of the axioms from recorded RDF facts on the Web may be regarded as a case of inductive reasoning and ontology learning. The instances, represented by RDF triples, play the role of specific observations, from which axioms can be extracted by generalization. Based on the insight that discovering new knowledge is essentially an evolutionary, whereby hypotheses are generated by some heuristic mechanism and then tested against the available evidence, so that only the best hypotheses survive, we propose a model applying Grammatical Evolution, one type of evolutionary algorithm, to mine OWL axioms from an RDF data repository. In addition, we specialize the model for the specific problem of learning OWL class disjointness axioms, along with the experiments performed on DBpedia, one of the prominent examples of LOD. Furthermore, we use different axiom scoring functions based on possibility theory, which are well-suited to the open world assumption scenario of LOD, to evaluate the quality of discovered axioms. Specifically, we proposed a set of measures to build objective functions based on single-objective and multi-objective models, respectively. Finally, in order to validate it, the performance of our approach is evaluated against subjective and objective benchmarks, and is also compared to the main state-of-the-art systems. Note de contenu : 1- Introduction 2- Foundation 3- Literature review 4- Learning OWL axioms from RDF data 5- Axiom evaluation 6- Grammatical evolution models toward class disjointness axiom discovery 7- A multi-objective GE approach to class disjointness axioms discovery 8- Conclusions & perspectives Numéro de notice : 28614 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Côte d'Azur : 2021 Organisme de stage : I3S DOI : sans En ligne : https://hal.science/tel-03406784/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99492

Reproducible research and GIScience: An evaluation using GIScience conference papers / Franck O. Ostermann (2021)  

Public Titre : Reproducible research and GIScience: An evaluation using GIScience conference papers Type de document : Article/Communication Auteurs : Franck O. Ostermann, Auteur ; Daniel Nüst, Auteur ; Carlos Granell, Auteur ; Barbara Hofer, Auteur ; Markus Konkol, Auteur Editeur : Leibniz [Allemagne] : Schloss Dagstuhl – Leibniz-Zentrum für Informatik Année de publication : 2021 Conférence : GIScience 2021, 11th International Conference on Geographic Information Science 27/09/2021 30/09/2021 Poznań Pologne Open Access Proceedings Importance : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Société de l'information [Termes IGN] code source libre [Termes IGN] données ouvertes [Termes IGN] information géographique [Termes IGN] recherche scientifique [Termes IGN] reproductibilité Résumé : (auteur) GIScience conference authors and researchers face the same computational reproducibility challenges as authors and researchers from other disciplines who use computers to analyse data. Here, to assess the reproducibility of GIScience research, we apply a rubric for assessing the reproducibility of 75 conference papers published at the GIScience conference series in the years 2012-2018. Since the rubric and process were previously applied to the publications of the AGILE conference series, this paper itself is an attempt to replicate that analysis, however going beyond the previous work by evaluating and discussing proposed measures to improve reproducibility in the specific context of the GIScience conference series. The results of the GIScience paper assessment are in line with previous findings: although descriptions of workflows and the inclusion of the data and software suffice to explain the presented work, in most published papers they do not allow a third party to reproduce the results and findings with a reasonable effort. We summarise and adapt previous recommendations for improving this situation and propose the GIScience community to start a broad discussion on the reusability, quality, and openness of its research. Further, we critically reflect on the process of assessing paper reproducibility, and provide suggestions for improving future assessments. The code and data for this article are published at https://doi.org/10.5281/zenodo.4032875. Numéro de notice : C2021-080 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE/SOCIETE NUMERIQUE Nature : Communication DOI : 10.4230/LIPIcs.GIScience.2021.II.2 Date de publication en ligne : 14/09/2021 En ligne : https://doi.org/10.4230/LIPIcs.GIScience.2021.II.2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100938

Spatial dataset search: Building a dedicated knowledge graph / Mehdi Zrhal (2021)  

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A context sensitive approach to anonymizing public participation GIS data: From development to the assessment of anonymization effects on data quality / Kamyar Hasanzadeh in Computers, Environment and Urban Systems, vol 83 (September 2020)  

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Cartographie des surfaces pastorales à l’aide des données Sentinel 2 L3A et des données ouvertes : Promesses et réalités / Urcel Kalenga Tshingomba in Revue internationale de géomatique, vol 30 n° 3-4 (juillet - décembre 2020)  

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Conciliating perspectives from mapping agencies and web of data on successful European SDIs: toward a European geographic knowledge graph / Bénédicte Bucher in ISPRS International journal of geo-information, vol 9 n° 2 (February 2020)  

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Data scale as cartography: a semi-automatic approach for thematic web map creation / Auriol Degbelo in Cartography and Geographic Information Science, vol 47 n° 2 (February 2020)  

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Estuaries and coastal zones / Jiayi Pan (2020)  

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Open data, big data, décisionnel, etc. : quels impacts sur la place de l'entité SIG des collectivités ? / Mathieu Le Moal in Géomatique expert, n° 132-133 (janvier - septembre 2020)

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Open scientific data / Vera J. Lipton (2020)  

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Projet infos usagers et projet transport à la demande / Oriane Sobreira (2020) 

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Refondre les politiques publiques avec le numérique / Gilles Babinet (2020)

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