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3D tree modeling from incomplete point clouds via optimization and L1-MST / Jie Mei in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)
[article]
Titre : 3D tree modeling from incomplete point clouds via optimization and L1-MST Type de document : Article/Communication Auteurs : Jie Mei, Auteur ; Liqiang Zhang, Auteur ; Shihao Wu, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 999 - 1021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme STA
[Termes IGN] arbre (flore)
[Termes IGN] branche (arbre)
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] méthode robuste
[Termes IGN] modèle numérique d'objet
[Termes IGN] optimisation (mathématiques)
[Termes IGN] semis de points
[Termes IGN] semis de points clairsemés
[Termes IGN] squelettisationRésumé : (auteur) Reconstruction of 3D trees from incomplete point clouds is a challenging issue due to their large variety and natural geometric complexity. In this paper, we develop a novel method to effectively model trees from a single laser scan. First, coarse tree skeletons are extracted by utilizing the L1-median skeleton to compute the dominant direction of each point and the local point density of the point cloud. Then we propose a data completion scheme that guides the compensation for missing data. It is an iterative optimization process based on the dominant direction of each point and local point density. Finally, we present a L1-minimum spanning tree (MST) algorithm to refine tree skeletons from the optimized point cloud, which integrates the advantages of both L1-median skeleton and MST algorithms. The proposed method has been validated on various point clouds captured from single laser scans. The experiment results demonstrate the effectiveness and robustness of our method for coping with complex shapes of branching structures and occlusions. Numéro de notice : A2017-239 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1264075 En ligne : http://dx.doi.org/10.1080/13658816.2016.1264075 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85173
in International journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 999 - 1021[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2017031 RAB Revue Centre de documentation En réserve L003 Disponible Reconstruction of itineraries from annotated text with an informed spanning tree algorithm / Ludovic Moncla in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)
[article]
Titre : Reconstruction of itineraries from annotated text with an informed spanning tree algorithm Type de document : Article/Communication Auteurs : Ludovic Moncla , Auteur ; Mauro Gaio, Auteur ; Javier Nogueras-Iso, Auteur ; Sébastien Mustière , Auteur Année de publication : 2016 Projets : 3-projet - voir note / Article en page(s) : pp 1137 - 1160 Note générale : Bibliographie
projet PerdidoLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] algorithme STA
[Termes IGN] analyse multicritère
[Termes IGN] approximation
[Termes IGN] arbre de décision
[Termes IGN] graphe
[Termes IGN] langage naturel (informatique)
[Termes IGN] reconstruction d'itinéraire ou de trajectoire
[Termes IGN] traitement du langage naturelRésumé : (Auteur) Considerable amounts of geographical data are still collected not in form of GIS data but just as natural language texts. This paper proposes an approach for the automatic geocoding of itineraries described in natural language. This approach needs as an input a text annotated with part-of-speech and geo-semantic tags. The proposed method is divided into three main steps. First, we build a complete graph where vertices represent locations, and all vertices are connected to each other by undirected edges. We assign a weight to all the edges of the complete graph using a multi-criteria analysis approach. Then we compute a minimum spanning tree to obtain an undirected acyclic graph connecting all vertices. And finally, we transform this graph into a partially directed acyclic graph in order to identify the sequence of waypoints and build an approximation of a plausible footprint of the itinerary described. Additionally, the rationale of the proposed approach has been verified with a set of experiments on a corpus of hiking descriptions. Numéro de notice : A2016-297 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1108422 Date de publication en ligne : 09/11/2015 En ligne : https://doi.org/10.1080/13658816.2015.1108422 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80883
in International journal of geographical information science IJGIS > vol 30 n° 5-6 (May - June 2016) . - pp 1137 - 1160[article]Voir aussiRéservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2016032 RAB Revue Centre de documentation En réserve L003 Disponible 079-2016031 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Statistical learning from a regression perspective Type de document : Guide/Manuel Auteurs : Richard A. Berk, Auteur Editeur : Springer International Publishing Année de publication : 2016 ISBN/ISSN/EAN : 978-3-319-44048-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse de données
[Termes IGN] arbre aléatoire
[Termes IGN] classification et arbre de régression
[Termes IGN] ensachage
[Termes IGN] régression
[Termes IGN] régression multivariée par spline adaptative
[Termes IGN] régression par quantile
[Termes IGN] séparateur à vaste margeRésumé : (éditeur) This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be treated as a form of regression analysis. Key concepts and procedures are illustrated with real applications, especially those with practical implications. A principal instance is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Also provided is helpful craft lore such as not automatically ceding data analysis decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important message is to appreciate the limitation of one’s data and not apply statistical learning procedures that require more than the data can provide. The material is written for upper undergraduate level and graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. The author uses this book in a course on modern regression for the social, behavioral, and biological sciences. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R with code routinely provided. Note de contenu : 1- Statistical Learning as a Regression Problem
2- Splines, Smoothers, and Kernels
3- Classification and Regression Trees (CART)
4- Bagging
5- Random Forests
6- Boosting
7- Support Vector Machines
8- Some Other Procedures Briefly
9- Broader Implications and a Bit of Craft LoreNuméro de notice : 25800 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Manuel de cours DOI : 10.1007/978-3-319-44048-4 En ligne : https://doi.org/10.1007/978-3-319-44048-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95043
contenu dans Geographic Information Science, 8th International Conference, GIScience 2014, Vienna Austria, September 24-26, 2014 / Matt Duckham (2014)
Titre : Automatic itinerary reconstruction from texts Type de document : Article/Communication Auteurs : Ludovic Moncla , Auteur ; Mauro Gaio, Auteur ; Sébastien Mustière , Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2014 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 8728 Conférence : GIScience 2014, 8th international conference on geographic information science 23/09/2014 26/09/2014 Vienne Autriche Proceedings Springer Importance : pp 253 - 267 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] algorithme STA
[Termes IGN] calcul d'itinéraire
[Termes IGN] itinéraire
[Termes IGN] langage naturel (informatique)
[Termes IGN] traitement du langage naturelRésumé : (auteur) This paper proposes an approach for the reconstruction of itineraries extracted from narrative texts. This approach is divided into two main tasks. The first extracts geographical information with natural language processing. Its outputs are annotations of so called expanded entities and expressions of displacement or perception from hiking descriptions. In order to reconstruct a plausible footprint of an itinerary described in the text, the second task uses the outputs of the first task to compute a minimum spanning tree. Numéro de notice : C2014-004 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-319-11593-1_17 En ligne : http://dx.doi.org/10.1007/978-3-319-11593-1_17 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78475 Verification of 2D building outlines using oblique airborne images / A. Nyaruhuma in ISPRS Journal of photogrammetry and remote sensing, vol 71 (July 2012)
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Titre : Verification of 2D building outlines using oblique airborne images Type de document : Article/Communication Auteurs : A. Nyaruhuma, Auteur ; Markus Gerke, Auteur ; M. George Vosselman, Auteur ; E.G. Mtalo, Auteur Année de publication : 2012 Article en page(s) : pp 62 - 75 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] arbre aléatoire
[Termes IGN] base de données foncières
[Termes IGN] bâtiment
[Termes IGN] boosting adapté
[Termes IGN] classification de Dempster-Shafer
[Termes IGN] contour
[Termes IGN] image aérienne oblique
[Termes IGN] logique floueRésumé : (Auteur) Oblique airborne images are interesting not only for visualization but also for the acquisition and updating of geo-spatial vector data. This is because side views of vertical structures, such as buildings, are present in those images. In recent years, techniques for automatic verification of building outlines have been proposed. These techniques utilized color, texture and height from vertical images or range data while oblique images contain façade information that can also be used to identify buildings. This paper presents a methodology to verify 2D building outlines in a cadastral dataset by using oblique airborne images. The method searches for clues such as building edges, wall façade edges and texture. The 2D clues in images taken from different perspectives but expected to contain the same wall are transformed to 3D, combined and used for a verification of the particular wall. Unlike methods that use vertical images or LIDAR, walls are verified individually and then the results are combined for the building. We compare three methods for combining wall-based evidence. Experiments using almost 700 buildings show that best results are obtained using Adaptive Boosting where – with a bias for better identification of demolished buildings – 100% of demolished buildings are identified and 91% of existing buildings are confirmed. The other two methods are Random Trees and a variant of the Dempster–Shafer approach combined with fuzzy reasoning and they only show some minor differences to the Adaptive Boosting result. The research as presented in this paper demonstrates the potential of oblique images, but some further work has to be done, including the identification of modified buildings and the extension towards verification of 3D building models. Numéro de notice : A2012-348 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.04.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.04.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31794
in ISPRS Journal of photogrammetry and remote sensing > vol 71 (July 2012) . - pp 62 - 75[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2012051 SL Revue Centre de documentation Revues en salle Disponible Efficient regionalization techniques for socio-economic geographical units using minimum spanning trees / Renato Martins Assuncao in International journal of geographical information science IJGIS, vol 20 n° 7 (august 2006)PermalinkBuilding displacement over a ductile truss / M. Bader in International journal of geographical information science IJGIS, vol 19 n° 8 - 9 (september 2005)PermalinkBuilt-up area analysis / Nicolas Regnauld (1995)Permalink