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Comparison of four line-based positional assessment methods by means of synthetic data / F. Arira-Lopez in Geoinformatica, vol 16 n° 2 (April 2012)  

Public [article]  inGeoinformatica > vol 16 n° 2 (April 2012) . - pp 221 - 243 Titre : Comparison of four line-based positional assessment methods by means of synthetic data Type de document : Article/Communication Auteurs : F. Arira-Lopez, Auteur ; A. Mozas-Calvache, Auteur Année de publication : 2012 Article en page(s) : pp 221 - 243 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] distance de Hausdorff [Termes IGN] estimation de position [Termes IGN] qualité des données [Termes IGN] valeur aberrante [Termes IGN] zone tampon Résumé : (Auteur) Positional accuracy of spatial data can be assessed by means of line-based methods. In this work we develop an analysis of the following four methods: Hausdorff Distance, Mean Distance, Single Buffer Overlay and Double Buffer Overlay, using a set of 12 synthetic cases. The synthetic cases incorporate specific shape features for bias, random errors and outliers which correspond to simplified versions of real world possibilities. The use of synthetic cases helps us to understand the basic behavioral differences between the methods. Numerical results for the positional accuracy estimations are different between methods and cases due to the different concepts of distance involved and the specific configurations of each case. When the method results in a function, patterns related to different types of errors can be detected in this function. The length-inclusion level of each method is revealed as the base criterion for comparison. The Single Buffer Overlay Method offers the more general solution because it includes the others’ results. Numéro de notice : A2012-087 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-011-0130-y Date de publication en ligne : 01/07/2011 En ligne : https://doi.org/10.1007/s10707-011-0130-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31535 [article]

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Blind and squaring-resistant watermarking of vectorial building layers / Julien Lafaye in Geoinformatica, vol 16 n° 2 (April 2012)    

Public [article]  inGeoinformatica > vol 16 n° 2 (April 2012) . - pp 245 - 279 Titre : Blind and squaring-resistant watermarking of vectorial building layers Type de document : Article/Communication Auteurs : Julien Lafaye, Auteur ; Jean Béguec, Auteur ; David Gross-Amblard, Auteur ; Anne Ruas , Auteur Année de publication : 2012 Article en page(s) : pp 245 - 279 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] base de données localisées [Termes IGN] bati [Termes IGN] droit d'auteur [Termes IGN] tatouage numérique Résumé : (Auteur) Due to the ease of digital copy, watermarking is crucial to protect the intellectual property of rights owners. We propose an effective watermarking method for vectorial geographical databases, with the focus on the buildings layer. Embedded watermarks survive common geographical filters, including the essential squaring and simplification transformations, as well as deliberate removal attempts, e.g. by noise addition, cropping or over-watermarking. Robustness against the squaring transformation is not addressed by existing approaches. The impact on the quality of the data sets, defined as a composition of point accuracy and angular quality, is assessed through an extensive series of experiments. Our method is based on a quantization of the distance between the centroid of the building and its extremal vertex according to its orientation. Numéro de notice : A2012-088 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-011-0133-8 Date de publication en ligne : 06/07/2011 En ligne : https://doi.org/10.1007/s10707-011-0133-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31536 [article]

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Documents numériques

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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]

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An interactive framework for spatial joins : a statistical approach to data analysis in GIS / S. Alkobaisi in Geoinformatica, vol 16 n° 2 (April 2012)  

Public [article]  inGeoinformatica > vol 16 n° 2 (April 2012) . - pp 329 - 355 Titre : An interactive framework for spatial joins : a statistical approach to data analysis in GIS Type de document : Article/Communication Auteurs : S. Alkobaisi, Auteur ; W. Bae, Auteur ; P. Vojtechovsky, Auteur ; S. Narayanappa, Auteur Année de publication : 2012 Article en page(s) : pp 329 - 355 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de données [Termes IGN] arbre quadratique [Termes IGN] arbre-R [Termes IGN] système d'information géographique Résumé : (Auteur) Many Geographic Information Systems (GIS) handle a large volume of geospatial data. Spatial joins over two or more geospatial datasets are very common operations in GIS for data analysis and decision support. However, evaluating spatial joins can be very time intensive due to the size of datasets. In this paper, we propose an interactive framework that provides faster approximate answers of spatial joins. The proposed framework utilizes two statistical methods: probabilistic join and sampling based join. The probabilistic join method provides speedup of two orders of magnitude with no correctness guarantee, while the sampling based method provides an order of magnitude improvement over the full indexing tree joins of datasets and also provides running confidence intervals. The framework allows users to trade-off speed versus bounded accuracy, hence it provides truly interactive data exploration. The two methods are evaluated empirically with real and synthetic datasets. Numéro de notice : A2012-090 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-011-0134-7 Date de publication en ligne : 19/08/2011 En ligne : https://doi.org/10.1007/s10707-011-0134-719/08/2011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31538 [article]

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Efficient parallel algorithm for pixel classification in remote sensing imagery / U. Maulik in Geoinformatica, vol 16 n° 2 (April 2012)  

Public [article]  inGeoinformatica > vol 16 n° 2 (April 2012) . - pp 391 - 407 Titre : Efficient parallel algorithm for pixel classification in remote sensing imagery Type de document : Article/Communication Auteurs : U. Maulik, Auteur ; A. Sarkar, Auteur Année de publication : 2012 Article en page(s) : pp 391 - 407 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] classification barycentrique [Termes IGN] classification dirigée [Termes IGN] image IRS [Termes IGN] image SPOT [Termes IGN] pixel [Termes IGN] traitement parallèle Résumé : (Auteur) An important approach for image classification is the clustering of pixels in the spectral domain. Fast detection of different land cover regions or clusters of arbitrarily varying shapes and sizes in satellite images presents a challenging task. In this article, an efficient scalable parallel clustering technique of multi-spectral remote sensing imagery using a recently developed point symmetry-based distance norm is proposed. The proposed distributed computing time efficient point symmetry based K-Means technique is able to correctly identify presence of overlapping clusters of any arbitrary shape and size, whether they are intra-symmetrical or inter-symmetrical in nature. A Kd-tree based approximate nearest neighbor searching technique is used as a speedup strategy for computing the point symmetry based distance. Superiority of this new parallel implementation with the novel two-phase speedup strategy over existing parallel K-Means clustering algorithm, is demonstrated both quantitatively and in computing time, on two SPOT and Indian Remote Sensing satellite images, as even K-Means algorithm fails to detect the symmetry in clusters. Different land cover regions, classified by the algorithms for both images, are also compared with the available ground truth information. The statistical analysis is also performed to establish its significance to classify both satellite images and numeric remote sensing data sets, described in terms of feature vectors. Numéro de notice : A2012-094 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-011-0136-5 Date de publication en ligne : 06/09/2011 En ligne : https://doi.org/10.1007/s10707-011-0136-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31542 [article]

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