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Learning sequential slice representation with an attention-embedding network for 3D shape recognition and retrieval in MLS point clouds / Zhipeng Luo in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 147 - 163 Titre : Learning sequential slice representation with an attention-embedding network for 3D shape recognition and retrieval in MLS point clouds Type de document : Article/Communication Auteurs : Zhipeng Luo, Auteur ; Di Liu, Auteur ; Jonathan Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 147 - 163 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] balayage laser [Termes descripteurs IGN] données laser [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] graphe [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] télémétrie laser mobile Résumé : (Auteur) The representation of 3D data is the key issue for shape analysis. However, most of the existing representations suffer from high computational cost and structure information loss. This paper presents a novel sequential slice representation with an attention-embedding network, named RSSNet, for 3D point cloud recognition and retrieval in road environments. RSSNet has two main branches. Firstly, a sequential slice module is designed to map disordered 3D point clouds to ordered sequence of shallow feature vectors. A gated recurrent unit (GRU) module is applied to encode the spatial and content information of these sequential vectors. The second branch consists of a key-point based graph convolution network (GCN) with an embedding attention strategy to fuse the sequential and global features to refine the structure discriminability. Three datasets were used to evaluate the proposed method, one acquired by our mobile laser scanning (MLS) system and two public datasets (KITTI and Sydney Urban Objects). Experimental results indicated that the proposed method achieved better performance than recognition and retrieval state-of-the-art methods. RSSNet provided recognition rates of 98.08%, 95.77% and 70.83% for the above three datasets, respectively. For the retrieval task, RSSNet obtained excellent mAP values of 95.56%, 87.16% and 69.99% on three datasets, respectively. Numéro de notice : A2020-064 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.003 date de publication en ligne : 22/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.003 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94582 [article]

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Fast computation of distances in a tree / Marc Pierrot-Deseilligny (2020)    

Public contenu dans HAL Hyper articles en ligne / Centre pour la Communication Scientifique Directe CCSD (2000)   Titre : Fast computation of distances in a tree Type de document : Article/Communication Auteurs : Marc Pierrot-Deseilligny , Auteur Editeur : Paris : HAL Année de publication : 2020 Importance : 8 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Algorithmique [Termes descripteurs IGN] arbre (mathématique) [Termes descripteurs IGN] distance (mathématique) Résumé : (Auteur) Computation of distances between two submits of a tree is an operation that occurs in some pattern recognition problem. When this operation has to be done thousands of times on millions of trees, the linear standard algorithms in OpN q for each pair may be a bottleneck to the global computation. This note present recursive spliting method with a complexity of OplogpN qq on each pair in worst case, and Op1q in average on all pair, once a pre-computation OpN logpN qq has been done on the whole tree. A commented C++ implementation is published as a companion to this note. 1 Numéro de notice : P2020-004 Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Preprint nature-HAL : Préprint DOI : sans date de publication en ligne : 05/05/2020 En ligne : https://hal.archives-ouvertes.fr/hal-02563859 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95036

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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]

Analysis of collaboration networks in OpenStreetMap through weighted social multigraph mining / Quy Thy Truong in International journal of geographical information science IJGIS, vol 33 n° 7 - 8 (July - August 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n° 7 - 8 (July - August 2019) . - pp 1651 - 1682 Titre : Analysis of collaboration networks in OpenStreetMap through weighted social multigraph mining Type de document : Article/Communication Auteurs : Quy Thy Truong , Auteur ; Cyril de Runz, Auteur ; Guillaume Touya , Auteur Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : pp 1651 - 1682 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] cartographie collaborative [Termes descripteurs IGN] comportement [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] graphe [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] pondération [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] travail coopératif Résumé : (auteur) This paper aims to qualify the behaviour of contributors to OpenStreetMap (OSM), a volunteered geographic information (VGI) project, through a multigraph approach. The main purpose is to reproduce contributor’s interactions in a more comprehensive way. First, we define a multigraph that combines existing spatial collaboration networks from the literature with new graphs that illustrate collaboration based on specific aspects of the VGI modes of contribution through semantics, geometry and topology. Indeed, the ways that contributors interact with one another through editing, completion, or even consumption may provide additional information on each user’s operation mode and therefore, on the quality of the contributed data. Social collaborations drawn from indirect criteria – for example, comparisons between contributors’ activity areas – can also be contemplated under another network. Second, the resulting multigraph is analysed using data mining approaches to characterise individuals and identify behavioural groups. The implementation of a multiplex network based on an OSM data sample and an initial analysis make it possible to identify useful behaviours for data qualification. The initial results characterise some contributors as pioneers, moderators and truthful contributors, according to their special roles in the graphs. Mapping elements that include these contributors’ participation are likely to be reliable data. Numéro de notice : A2019-025 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1556395 date de publication en ligne : 17/12/2018 En ligne : https://doi.org/10.1080/13658816.2018.1556395 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91958 [article]

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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]

Deeply integrating linked data with geographic information systems / Gengchen Mai in Transactions in GIS, vol 23 n° 3 (June 2019)  

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Piecewise-planar approximation of large 3D data as graph-structured optimization / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W5 (may 2019)  

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Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets / Yusheng Xu in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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Central place indexing : hierarchical linear indexing systems for mixed-aperture hexagonal discrete global grid systems / Kevin Sahr in Cartographica, vol 54 n° 1 (spring 2019)  

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A graph-based approach for the structural analysis of road and building layouts / Mathieu Domingo in Geo-spatial Information Science, vol 22 n° 1 (March 2019)  

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A derivative-free optimization-based approach for detecting architectural symmetries from 3D point clouds / Fan Xue in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)  

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Improving LiDAR classification accuracy by contextual label smoothing in post-processing / Nan Li in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)  

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Point clouds for direct pedestrian pathfinding in urban environments / Jesus Balado in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)  

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Machine learning for image segmentation / Kaiwen Chang (2019)  

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Point cloud oversegmentation with graph-structured deep metric learning / Loïc Landrieu (2019)  

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