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Model driven reconstruction of roofs from sparse LIDAR point clouds / A. Henn in ISPRS Journal of photogrammetry and remote sensing, vol 76 (February 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 76 (February 2013) . - pp 17 - 29 Titre : Model driven reconstruction of roofs from sparse LIDAR point clouds Type de document : Article/Communication Auteurs : A. Henn, Auteur ; G. Groger, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 17 - 29 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données lidar [Termes IGN] empreinte [Termes IGN] Ransac (algorithme) [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points clairsemés [Termes IGN] toit Résumé : (Auteur) This article presents a novel, fully automatic method for the reconstruction of three-dimensional building models with prototypical roofs (CityGML LoD2) from LIDAR data and building footprints. The proposed method derives accurate results from sparse point data sets and is suitable for large area reconstruction. Sparse LIDAR data are widely available nowadays. Robust estimation methods such as RANSAC/MSAC, are applied to derive best fitting roof models in a model-driven way. For the identification of the most probable roof model, supervised machine learning methods (Support Vector Machines) are used. In contrast to standard approaches (where the best model is selected via MDL or AIC), supervised classification is able to incorporate additional features enabling a significant improvement in model selection accuracy. Numéro de notice : A2013-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.11.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.11.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32227 [article]

Exemplaires(1)

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081-2013021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Automatic classification of building types in 3D city models: Using SVMs for semantic enrichment of low resolution building data / A. Henn in Geoinformatica, vol 16 n° 2 (April 2012)  

Public [article]  inGeoinformatica > vol 16 n° 2 (April 2012) . - pp 281 - 306 Titre : Automatic classification of building types in 3D city models: Using SVMs for semantic enrichment of low resolution building data Type de document : Article/Communication Auteurs : A. Henn, Auteur ; C. Römer, Auteur ; G. Groger, Auteur ; L. Plumer, Auteur Année de publication : 2012 Article en page(s) : pp 281 - 306 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage automatique [Termes IGN] attribut sémantique [Termes IGN] bati [Termes IGN] classification automatique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image à basse résolution [Termes IGN] modèle 3D de l'espace urbain Résumé : (Auteur) This article presents a classifier based on Support Vector Machines (SVMs), an advanced machine learning method for semantic enrichment of coarse 3D city models by deriving the building type. The information on the building type (detached building, terraced building, etc.) is essential for a variety of relevant applications of 3D city models like spatial marketing, real estate management and marketing, and for visualization. The derivation of the building type from coarse data (mainly 2D footprints, building heights and functions) seems impossible at first sight. However it succeeds by incorporating the spatial context of a building. Since the input data can be derived easily and at very low cost, this method is widely applicable. Nevertheless, as with all supervised learning algorithms, obtaining labelled training data is very time-consuming. Herewith, we provide a method which uses outlier detection and clustering methods to support users in efficiently and rapidly obtaining adequate training data. Numéro de notice : A2012-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-011-0131-x Date de publication en ligne : 07/07/2011 En ligne : https://doi.org/10.1007/s10707-011-0131-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31537 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-2012021	RAB	Revue	Centre de documentation	En réserve L003	Disponible