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A topology-based graph data model for indoor spatial-social networking / Mahdi Rahimi in International journal of geographical information science IJGIS, vol 35 n° 12 (December 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 12 (December 2021) . - pp 2517 - 2539 Titre : A topology-based graph data model for indoor spatial-social networking Type de document : Article/Communication Auteurs : Mahdi Rahimi, Auteur ; Mohammad Reza Malek, Auteur ; Christophe Claramunt, Auteur ; Thierry Le Pors, Auteur Année de publication : 2021 Article en page(s) : pp 2517 - 2539 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] algorithme du simplexe [Termes IGN] espace intérieur [Termes IGN] graphe [Termes IGN] modèle topologique de données [Termes IGN] modélisation spatiale [Termes IGN] représentation géométrique [Termes IGN] représentation graphique [Termes IGN] réseau social géodépendant Résumé : (auteur) This paper introduces a simplex-based enriched graph data model integrating a discrete and place-based indoor spatial model with a spatial-social network. The proposed model incorporates similarity and relevance measures, exhibited from Q-analysis of simplicial complexes, facilitating data manipulation and revealing latent relations in a spatial-social network. It also uses an indoor-specific metric representing the ease of access to process spatial-social queries in indoor environments. The proposed model’s experimental implementation shows the quantitative advantage of using graph-based representation and the qualitative superiority of simplex-based enrichment in processing spatial-social queries in indoor environments. Numéro de notice : A2021-875 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1912349 Date de publication en ligne : 14/04/2021 En ligne : https://doi.org/10.1080/13658816.2021.1912349 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99138 [article]

Study on offshore seabed sediment classification based on particle size parameters using XGBoost algorithm / Fengfan Wang in Computers & geosciences, vol 149 (April 2021)  

Public [article]  inComputers & geosciences > vol 149 (April 2021) . - n° 104713 Titre : Study on offshore seabed sediment classification based on particle size parameters using XGBoost algorithm Type de document : Article/Communication Auteurs : Fengfan Wang, Auteur ; Jia Yu, Auteur ; Zhijie Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 104713 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse spatiale [Termes IGN] calcul matriciel [Termes IGN] Chine [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] diagramme [Termes IGN] échantillon [Termes IGN] Extreme Gradient Machine [Termes IGN] fond marin [Termes IGN] gravier [Termes IGN] image à haute résolution [Termes IGN] sédiment Résumé : (auteur) Folk's textual classification scheme which is widely used for sediment study operates with the proportions of gravel, sand, silt and clay fractions conventionally. However, dealing with data from different sources usually needs to face missing values that may make the classification difficult. To solve this problem and discover other methods of analyzing the scheme, with samples of offshore seabed sediment, a two-stage model was established to predict a sample's class using the XGBoost algorithm as well as the grain size parameters as input features. The final model was evaluated with quantitative performance measures of recall, precision and F1 score, and by comparing sediment texture maps using the predicted and the actual data. The results show that the model performs well on extraction of sediment samples without gravel fraction, and prediction of classes that have independent characteristics of grain size parameters or samples not near the boundaries of classes in the ternary diagram. The predicted sediment texture is close to the actual and could be reliable due to errors with little impact on further applications. It is demonstrated that the model could be an auxiliary or alternative approach to offshore sediment texture mapping, as well as supplementary to the analysis of sedimentary environment. Numéro de notice : A2021-289 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104713 Date de publication en ligne : 12/02/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104713 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97400 [article]

Learning embeddings for cross-time geographic areas represented as graphs / Margarita Khokhlova (2021)  

Public Titre : Learning embeddings for cross-time geographic areas represented as graphs Type de document : Article/Communication Auteurs : Margarita Khokhlova, Auteur ; Nathalie Abadie , Auteur ; Valérie Gouet-Brunet , Auteur ; Liming Chen, Auteur Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2021 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : SAC 2021, 36th Annual ACM Symposium on Applied Computing 22/03/2021 26/03/2021 en ligne Proceedings ACM Importance : pp 559 - 568 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] arête [Termes IGN] classification par réseau neuronal [Termes IGN] entité géographique [Termes IGN] graphe flou [Termes IGN] image aérienne à axe vertical [Termes IGN] noeud [Termes IGN] relation spatiale [Termes IGN] représentation graphique [Termes IGN] réseau neuronal de graphes Résumé : (auteur) Geographic entities from the vertical aerial images can be viewed as discrete objects and represented as nodes in a graph, linked to each other by edges capturing their spatial relationships. Over time, the natural and man made landscape may evolve and thus also their graph representations. This paper addresses the challenging problem of the retrieval and fuzzy matching of graphs to localize near-identical geographical areas across time. Several use-case scenarios are proposed for the end-to-end learning of a graph embedding using Graph Neural Networks (GNN), along with an effective baseline without learning. The results demonstrate the efficiency of our approach, that enables efficient similarity reasoning for novel hand-engineered cross-time graph data. Code and data processing scripts are available online. Numéro de notice : C2021-002 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1145/3412841.3441936 En ligne : https://doi.org/10.1145/3412841.3441936 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97583

Group diagrams for representing trajectories / Maike Buchin in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 12 (December 2020) . - pp 2401 - 2433 Titre : Group diagrams for representing trajectories Type de document : Article/Communication Auteurs : Maike Buchin, Auteur ; Bernhard Kilgus, Auteur ; Andrea Kölzsch, Auteur Année de publication : 2020 Article en page(s) : pp 2401 - 2433 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse spatio-temporelle [Termes IGN] approximation [Termes IGN] base de données d'objets mobiles [Termes IGN] diagramme [Termes IGN] distance de Fréchet [Termes IGN] données GPS [Termes IGN] géomètrie algorithmique [Termes IGN] itinéraire [Termes IGN] migration animale [Termes IGN] objet mobile Résumé : (auteur) Given the trajectories of one or several moving groups, we propose a new framework, the group diagram (GD) for representing these. Specifically, we seek a minimal GD as a concise representation of the groups maintaining the spatio-temporal structure of the groups’ movement. A GD is specified by three input values, namely a distance threshold, a similarity measure and a minimality criterion. For several variants of the GD, we give a comprehensive analysis of their computational complexity and present efficient approximation algorithms for their computation. Furthermore, we experimentally evaluate our algorithms on GPS data of migrating geese. Applying the proposed methods on these data sets reveals how the GD concisely represents the movement of the groups. This representation can be used for further analysis and for the formulation of new hypotheses for further ecological research, such as differences in movement patterns of groups on different surfaces or the shift of migration routes over several years. We use different similarity measures to summarize the migration routes of (i) a goose family for one migration period and to summarize (ii) the migration routes of one individual for several migration periods or (iii) the migration routes of several independent individuals for one migration period. Numéro de notice : A2020-690 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1684498 Date de publication en ligne : 25/11/2019 En ligne : https://doi.org/10.1080/13658816.2019.1684498 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96227 [article]

Micro diagrams: visualization of categorical point data from location-based social media / Mathias Gröbe in Cartography and Geographic Information Science, Vol 47 n° 4 (July 2020)  

Public [article]  inCartography and Geographic Information Science > Vol 47 n° 4 (July 2020) . - pp 305-320 Titre : Micro diagrams: visualization of categorical point data from location-based social media Type de document : Article/Communication Auteurs : Mathias Gröbe, Auteur ; Dirk Burghardt, Auteur Année de publication : 2020 Article en page(s) : pp 305-320 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatiale [Termes IGN] diagramme [Termes IGN] données localisées des bénévoles [Termes IGN] réseau social [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) Location-based social media data from different platforms such as Twitter and Flickr increasingly serve with their point-geocoded content as data sources for a variety of applications. The standard visualization method uses a derivation of point maps, which works well with a limited amount of data, but it suffers from weaknesses related to cluttering and overlapping, especially for sets of categories. We developed a new visualization method for categorical point data, called “Micro Diagrams”, which uses small diagrams to show the percentages of categories and the spatial distribution. The processing steps to derive the micro diagrams start with aggregating the points in a regular grid structure, which is followed by the selection of the diagram type that represents the numerical proportions and the application of a size scaling function to show the amounts of data. Various parameterization options are discussed and the influence of the color selection is analyzed. Finally, a case study combined with a user test presents the strengths and limits of the micro diagram method. Numéro de notice : A2020-189 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1733438 Date de publication en ligne : 10/03/2020 En ligne : https://doi.org/10.1080/15230406.2020.1733438 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95154 [article]

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