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A space-time varying graph for modelling places and events in a network / Ikechukwu Maduako in International journal of geographical information science IJGIS, vol 33 n° 10 (October 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n° 10 (October 2019) . - pp 1915 - 1935 Titre : A space-time varying graph for modelling places and events in a network Type de document : Article/Communication Auteurs : Ikechukwu Maduako, Auteur ; Monica Wachowicz, Auteur Année de publication : 2019 Article en page(s) : pp 1915 - 1935 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] accident de la route [Termes descripteurs IGN] analyse des risques [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] connexité (graphes) [Termes descripteurs IGN] graphe [Termes descripteurs IGN] relation topologique [Termes descripteurs IGN] représentation spatiale [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] voisinage (topologie) Résumé : (auteur) Modelling topological relationships between places and events is challenging especially because these relationships are dynamic, and their evolutionary analysis relies on the explanatory power of representing their interactions across different temporal resolutions. In this paper, we introduce the Space-Time Varying Graph (STVG) based on the whole graph approach that combines directed and bipartite subgraphs with a time-tree for representing the complex interaction between places and events across time. We demonstrate how the proposed STVG can be exploited to identify and extract evolutionary patterns of traffic accidents using graph metrics, ad-hoc graph queries and clustering algorithms. The results reveal evolutionary patterns that uncover the places with high incidence of accidents over different time resolutions, reveal the main reasons why the traffic accidents have occurred, and disclose evolving communities of densely connected traffic accidents over time. Numéro de notice : A2019-393 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1603386 date de publication en ligne : 17/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1603386 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93497 [article]

Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 246 - 258 Titre : Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information Type de document : Article/Communication Auteurs : Hao Fang, Auteur ; Florent Lafarge, Auteur Année de publication : 2019 Article en page(s) : pp 246 - 258 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] compréhension de l'image [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] prise en compte du contexte [Termes descripteurs IGN] représentation multiple [Termes descripteurs IGN] scène [Termes descripteurs IGN] scène intérieure [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] semis de points Résumé : (Auteur) Analyzing and extracting geometric features from 3D data is a fundamental step in 3D scene understanding. Recent works demonstrated that deep learning architectures can operate directly on raw point clouds, i.e. without the use of intermediate grid-like structures. These architectures are however not designed to encode contextual information in-between objects efficiently. Inspired by a global feature aggregation algorithm designed for images (Zhao et al., 2017), we propose a 3D pyramid module to enrich pointwise features with multi-scale contextual information. Our module can be easily coupled with 3D semantic segmentation methods operating on 3D point clouds. We evaluated our method on three large scale datasets with four baseline models. Experimental results show that the use of enriched features brings significant improvements to the semantic segmentation of indoor and outdoor scenes. Numéro de notice : A2019-271 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.010 date de publication en ligne : 01/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93089 [article]

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Multiscale cartographic visualization of harmonized datasets / Peter Kunz 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 178 - 194 Titre : Multiscale cartographic visualization of harmonized datasets Type de document : Article/Communication Auteurs : Peter Kunz, Auteur ; Joachim Bobrich, Auteur Congrès : Congrès: ICC 2019, 29th International Cartographic Conference ICA Mapping everything for everyone (15 - 20 juillet 2019; Tokyo, Japon) , Commanditaire Année de publication : 2019 Article en page(s) : pp 178 - 194 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Allemagne [Termes descripteurs IGN] cartographie topographique [Termes descripteurs IGN] échelle cartographique [Termes descripteurs IGN] geoportail [Termes descripteurs IGN] intégration de données [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] PostGIS [Termes descripteurs IGN] représentation multiple [Termes descripteurs IGN] stockage de données [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) With the product TopPlusOpen, the Federal Agency for Cartography and Geodesy (BKG) has for the first time provided a freely accessible web map, which is based entirely on open or free data sources. For this purpose, the BKG processed and combined many different data sources in order to achieve the best possible map presentation. Among others, free official spatial data of the Federal Government and the Open Data states Berlin, Hamburg, North Rhine-Westphalia and Thuringia are presented in the product. Furthermore, Brandenburg, Mecklenburg-Western Pomerania and Rhineland-Palatinate provide their official spatial data for TopPlusOpen based on a cooperation agreement so that also these states are presented exclusively by official data. The services of the TopPlusOpen were released in September 2017. The product was very well received by the public, private and scientific sectors and is already used as a background map in many geoportals. The free access and the cross-border map presentation are especially mentioned. Numéro de notice : A2019-236 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2019.1610931 date de publication en ligne : 13/05/2019 En ligne : https://doi.org/10.1080/23729333.2019.1610931 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92935 [article]

Computing and querying strict, approximate, and metrically refined topological relations in linked geographic data / Blake Regalia in Transactions in GIS, vol 23 n° 3 (June 2019)  

Public [article]  inTransactions in GIS > vol 23 n° 3 (June 2019) . - pp 601 - 619 Titre : Computing and querying strict, approximate, and metrically refined topological relations in linked geographic data Type de document : Article/Communication Auteurs : Blake Regalia, Auteur ; Krzysztof Janowicz, Auteur ; Grant McKenzie, Auteur Année de publication : 2019 Article en page(s) : pp 601 - 619 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] DBpedia [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] entité géographique [Termes descripteurs IGN] graphe [Termes descripteurs IGN] relation topologique [Termes descripteurs IGN] requête spatiale [Termes descripteurs IGN] Web des données Résumé : (Auteur) Geographic entities and the information associated with them play a major role in Web‐scale knowledge graphs such as Linked Data. Interestingly, almost all major datasets represent places and even entire regions as point coordinates. There are two key reasons for this. First, complex geometries are difficult to store and query using the current Linked Data technology stack to a degree where many queries take minutes to return or will simply time out. Second, the absence of complex geometries confirms a common suspicion among GIScientists, namely that for many everyday queries place‐based relational knowledge is more relevant than raw geometries alone. To give an illustrative example, the statement that the White House is in Washington, DC is more important for gaining an understating of the city than the exact geometries of both entities. This does not imply that complex geometries are unimportant but that (topological) relations should also be extracted from them. As Egenhofer and Mark (1995b) put it in their landmark paper on naive geography, topology matters, metric refines. In this work we demonstrate how to compute and utilize strict, approximate, and metrically refined topological relations between several geographic feature types in DBpedia and compare our results to approaches that compute result sets for topological queries on the fly. Numéro de notice : A2019-256 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12548 date de publication en ligne : 26/06/2019 En ligne : https://doi.org/10.1111/tgis.12548 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93014 [article]

Learning high-level features by fusing multi-view representation of MLS point clouds for 3D object recognition in road environments / Zhipeng Luo in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 150 (April 2019) . - pp 44 - 58 Titre : Learning high-level features by fusing multi-view representation of MLS point clouds for 3D object recognition in road environments Type de document : Article/Communication Auteurs : Zhipeng Luo, Auteur ; Jonathan Li, Auteur ; Zhenlong Xiao, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 44 - 58 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] jointure spatiale [Termes descripteurs IGN] objet 3D [Termes descripteurs IGN] reconnaissance d'objet [Termes descripteurs IGN] représentation multiple [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] semis de points Résumé : (Auteur) Most existing 3D object recognition methods still suffer from low descriptiveness and weak robustness although remarkable progress has made in 3D computer vision. The major challenge lies in effectively mining high-level 3D shape features. This paper presents a high-level feature learning framework for 3D object recognition through fusing multiple 2D representations of point clouds. The framework has two key components: (1) three discriminative low-level 3D shape descriptors for obtaining multi-view 2D representation of 3D point clouds. These descriptors preserve both local and global spatial relationships of points from different perspectives and build a bridge between 3D point clouds and 2D Convolutional Neural Networks (CNN). (2) A two-stage fusion network, which consists of a deep feature learning module and two fusion modules, for extracting and fusing high-level features. The proposed method was tested on three datasets, one of which is Sydney Urban Objects dataset and the other two were acquired by a mobile laser scanning (MLS) system along urban roads. The results obtained from comprehensive experiments demonstrated that our method is superior to the state-of-the-art methods in descriptiveness, robustness and efficiency. Our method achieves high recognition rates of 94.6%, 93.1% and 74.9% on the above three datasets, respectively. Numéro de notice : A2019-137 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.024 date de publication en ligne : 16/02/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.024 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92468 [article]

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An automated and optimized approach for online spatial biodiversity model: a case study of OGC web processing service / Hariom Singh in Geocarto international, vol 34 n° 2 ([01/02/2019])  

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Finding the oasis in the desert fog? Understanding multi-scale map reading / Guillaume Touya (2019)    

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Query rewriting for semantic query optimization in spatial databases / Eduardo Mella in Geoinformatica [en ligne], vol 23 n° 1 (January 2019)  

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Réorganisation du SIG et valorisation des données du Parc Naturel Régional du Gâtinais français / Paul Roux (2019) 

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Simultaneous chain-forming and generalization of road networks / Susanne Wenzel in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

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An algorithm for on-the-fly K shortest paths finding in multi-storey buildings using a hierarchical topology model / Rosen Ivanov in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)  

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A topology-preserving polygon rasterization algorithm / Chen Zhou in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)  

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Compactly representing massive terrain models as TINs in CityGML / Kavisha Kumar in Transactions in GIS, vol 22 n° 5 (October 2018)  

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A data model for moving regions of fixed shape in databases / Florian Heinz in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

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Élaboration d’un modèle de données spatiales à inspiration cognitive pour assister la navigation des piétons non voyants / Reda Yaagoubi in Revue internationale de géomatique, vol 28 n° 3 (juillet - septembre 2018)  

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