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Use of artificial neural networks for selective omission in updating road networks / Qi Zhou in Cartographic journal (the), vol 51 n° 1 (February 2014)
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Titre : Use of artificial neural networks for selective omission in updating road networks Type de document : Article/Communication Auteurs : Qi Zhou, Auteur ; Zhilin Li, Auteur Année de publication : 2014 Article en page(s) : pp 38 - 51 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] analyse comparative
[Termes IGN] carte de Kohonen
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] mise à jour automatique
[Termes IGN] mise à jour cartographique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] réseau routierRésumé : (Auteur) An important problem faced by national mapping agencies is frequent map updates. An ideal solution is only updating the large-scale map with other smaller scale maps undergoing automatic updates. This process may involve a series of operators, among which selective omission has received much attention. This study focuses on selective omission in a road network, and the use of an artificial neural network (i.e. a back propagation neural network, BPNN). The use of another type of artificial neural network (i.e. a self-organizing map, SOM) is investigated as a comparison. The use of both neural networks for selective omission is tested on a real-life road network. The use of a BPNN for practical application road updating is also tested. The results of selective omission are evaluated by overall accuracy. It is found that (1) the use of a BPNN can adaptively determine which and how many roads are to be retained at a specific scale, with an overall accuracy above 80%; (2) it may be hard to determine which and how many roads should be retained at a specific scale using an SOM. Therefore, the BPNN is more effective for selective omission in road updating. Numéro de notice : A2014-128 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1179/1743277413Y.0000000042 En ligne : https://doi.org/10.1179/1743277413Y.0000000042 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33033
in Cartographic journal (the) > vol 51 n° 1 (February 2014) . - pp 38 - 51[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 030-2014011 RAB Revue Centre de documentation En réserve L003 Disponible Active learning of user’s preferences estimation towards a personalized 3D navigation of geo-referenced scenes / Christos Yiakoumettis in Geoinformatica, vol 18 n° 1 (January 2014)
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Titre : Active learning of user’s preferences estimation towards a personalized 3D navigation of geo-referenced scenes Type de document : Article/Communication Auteurs : Christos Yiakoumettis, Auteur ; Nikolaos Doulamis, Auteur ; Georgios Miaoulis, Auteur ; Djamchid Ghazanfarpour, Auteur Année de publication : 2014 Article en page(s) : pp 27 - 62 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] algorithme génétique
[Termes IGN] apprentissage dirigé
[Termes IGN] Athènes
[Termes IGN] exploration de données
[Termes IGN] itinéraire
[Termes IGN] métadonnées
[Termes IGN] navigation
[Termes IGN] ontologie
[Termes IGN] optimisation (mathématiques)
[Termes IGN] personnalisation
[Termes IGN] pondération
[Termes IGN] réalité virtuelle
[Termes IGN] système d'information géographique
[Termes IGN] utilisateurRésumé : (Auteur) The current technological evolutions enter 3D geo-informatics into their digital age, enabling new potential applications in the field of virtual tourism, pleasure, entertainment and cultural heritage. It is argued that 3D information provides the natural way of navigation. However, personalization is a key aspect in a navigation system, since a route that incorporates user preferences is ultimately more suitable than the route with the shortest distance or travel time. Usually, user’s preferences are expressed as a set of weights that regulate the degree of importance of the scene metadata on the route selection process. These weights, however, are defined by the users, setting the complexity to the user’s side, which makes personalization an arduous task. In this paper, we propose an alternative approach in which metadata weights are estimated implicitly and transparently to the users, transferring the complexity to the system side. This is achieved by introducing a relevance feedback on-line learning strategy which automatically adjusts metadata weights by exploiting information fed back to the system about the relevance of user’s preferences judgments given in a form of pair-wise comparisons. Practically implementing a relevance feedback algorithm presents the limitation that several pair-wise comparisons (samples) are required to converge to a set of reliable metadata weights. For this reason, we propose in this paper a weight rectification strategy that improves weight estimation by exploiting metadata interrelations defined through an ontology. In the sequel, a genetic optimization algorithm is incorporated to select the most user preferred routes based on a multi-criteria minimization approach. To increase the degree of personalization in 3D navigation, we have also introduced an efficient algorithm for estimating 3D trajectories around objects of interest by merging best selected 2D projected views that contain faces which are mostly preferred by the users. We have conducted simulations and comparisons with other approaches either in the field of on-line learning or route selection using objective metrics in terms of precision and recall values. The results indicate that our system yields on average a 13.76 % improvement of precision as regards the learning strategy and an improvement of 8.75 % regarding route selection. In addition, we conclude that the ontology driven weight rectification strategy can reduce the number of samples (pair-wise comparisons) required of 76 % to achieve the same precision. Qualitative comparisons have been also performed using a use case route scenario in the city of Athens. Numéro de notice : A2014-027 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10707-013-0176-0 Date de publication en ligne : 12/04/2013 En ligne : https://doi.org/10.1007/s10707-013-0176-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32932
in Geoinformatica > vol 18 n° 1 (January 2014) . - pp 27 - 62[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-2014011 RAB Revue Centre de documentation En réserve L003 Disponible Agricultural field delimitation using active learning and random forests margin / Karim Ghariani (2014)
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Titre : Agricultural field delimitation using active learning and random forests margin Type de document : Article/Communication Auteurs : Karim Ghariani, Auteur ; Nesrine Chehata , Auteur ; Arnaud Le Bris
, Auteur ; Philippe Lagacherie, Auteur
Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2014 Conférence : IGARSS 2014, International Geoscience And Remote Sensing Symposium 13/07/2014 18/07/2014 Québec Québec - Canada Proceedings IEEE Importance : pp 1717 - 1720 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] délimitation
[Termes IGN] image Geoeye
[Termes IGN] surface cultivéeRésumé : (auteur) Agricultural practices and spatial arrangements of fields have a strong impact on water flows in cultivated landscapes. In order to monitor landscapes at a large scale, there is a strong need for automatic or semi-automatic field delineation. Field measurements for delineating parcel network are not efficient, thus very high resolution satellite imagery should help delineating agricultural fields in a automatic way. This study focuses on agricultural field delineation based on the classification of very high resolution satellite imagery. A hybrid approach is proposed and combines a region-based approach and active learning (AL) techniques. Random forest (RF) classifier is used for classification and feature selection. The margin concept is used as uncertainty measure in active learning algorithm. Satisfying results are shown on a Geoeye image. AL RF model is compared to simple and global RF models that are built from adjacent and geographically distant fields respectively. Numéro de notice : C2014-029 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2014.6946782 En ligne : http://dx.doi.org/10.1109/IGARSS.2014.6946782 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83401 Documents numériques
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Agricultural field delimitation - posterAdobe Acrobat PDFAssessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping / Luca Demarchi in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)
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Titre : Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping Type de document : Article/Communication Auteurs : Luca Demarchi, Auteur ; Frank Canters, Auteur ; Claude Cariou, Auteur ; Giorgio Licciardi, Auteur ; Jonathan Cheung-Wai Chan, Auteur Année de publication : 2014 Article en page(s) : pp 166 - 179 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Airborne Prism Experiment
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image APEX
[Termes IGN] image hyperspectrale
[Termes IGN] Perceptron multicoucheRésumé : (Auteur) Despite the high richness of information content provided by airborne hyperspectral data, detailed urban land-cover mapping is still a challenging task. An important topic in hyperspectral remote sensing is the issue of high dimensionality, which is commonly addressed by dimensionality reduction techniques. While many studies focus on methodological developments in data reduction, less attention is paid to the assessment of the proposed methods in detailed urban hyperspectral land-cover mapping, using state-of-the-art image classification approaches. In this study we evaluate the potential of two unsupervised data reduction techniques, the Autoassociative Neural Network (AANN) and the BandClust method – the first a transformation based approach, the second a feature-selection based approach – for mapping of urban land cover at a high level of thematic detail, using an APEX 288-band hyperspectral dataset. Both methods were tested in combination with four state-of-the-art machine learning classifiers: Random Forest (RF), AdaBoost (ADB), the multiple layer perceptron (MLP), and support vector machines (SVM). When used in combination with a strong learner (MLP, SVM) BandClust produces classification accuracies similar to or higher than obtained with the full dataset, demonstrating the method’s capability of preserving critical spectral information, required for the classifier to successfully distinguish between the 22 urban land-cover classes defined in this study. In the AANN data reduction process, on the other hand, important spectral information seems to be compromised or lost, resulting in lower accuracies for three of the four classifiers tested. Detailed analysis of accuracies at class level confirms the superiority of the SVM/Bandclust combination for accurate urban land-cover mapping using a reduced hyperspectral dataset. This study also demonstrates the potential of the new APEX sensor data for detailed mapping of land cover in spatially and spectrally complex urban areas. Numéro de notice : A2014-018 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.10.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.10.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32923
in ISPRS Journal of photogrammetry and remote sensing > vol 87 (January 2014) . - pp 166 - 179[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014011 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Automatische Interpretation von Semantik aus digitalen Karten im World Wide Web Type de document : Thèse/HDR Auteurs : Fen Luo, Auteur ; Dieter Fritsch, Directeur de thèse ; Ralf Bill, Directeur de thèse Editeur : Stuttgart : Institüt für Photogrammetrie der Universität Stuttgart Année de publication : 2014 Note générale : bibliographie
Von der Fakultät Luft- und Raumfahrttechnik und Geodäsie der Universität Stuttgart zur Erlangung der Würde eines Doktors der Ingenieurwissenschaften (Dr.-Ing.) genehmigte AbhandlungLangues : Allemand (ger) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] carte de Kohonen
[Termes IGN] données maillées
[Termes IGN] données vectorielles
[Termes IGN] échelle cartographique
[Termes IGN] exploration de données géographiques
[Termes IGN] objet géographique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] toile d'araignée mondialeRésumé : (auteur) On the Internet there are innumerable spatial data representing different sections of the world in form of raster and vector maps. The information contained in these maps is not automatically discoverable, since it is encoded by means of certain map elements. Its semantics is not explicit unless interpreted by an observer. However, the map information can be interpreted by not only humans but also ma-chines. This already requires the large amount of data to be interpreted. We are going to summarize the automatic derivation of semantics from the maps in terms of automatic map interpretation. It involves a process of making the implicit information of a map inventory explicit. For this purpose we present the map interpretation as solutions.
The map interpretation of the current study is done with vector maps what can be found on the internet. For the targeted search of vector maps of the internet, a web crawler is specially developed. The web crawler is a search engine that specifically looks for vector maps. For this, exclusively the shapefile format is sought, which has become a standard format in the GIS environment and in which the vector maps are usually stored. In order to find shapefiles as many as possible, the search is carried out on servers where the probability of finding shapefiles is high. These servers were previously found through the keyword “shapefile download” by Google search.
The maps interpretation includes methods of interpretation of the map objects, of the map types, and of the map scale. First, we will introduce the method of interpreting the map objects. Our aim is to automatically detect the objects based on their specific characteristics. The object recognition is based on self-organizing map (SOM) that is borrowed from artificial intelligence. The map objects are clas-sified into, for example, building floor plan and road network. Its own characteristics should be found for each class and brought in one of the accessible forms of SOM, in this case, a parameter vector. The parameter vectors form the input patterns that are learned in the training phase of SOM. After the input patterns of all object classes of SOM have been learned, the parameter vector is evaluated for each of the present objects on the map and given to the SOM. By the previously successful learning of the input pattern, the objects can be assigned based on each of their calculated parameter vectors of the corresponding object class.
The interpretation of map type is presented as another method. Maps are categorized into different types according to their substantive content and purpose, such as river maps, road maps, contour maps, etc. As for the interpretation of objects, SOM is used here. Hence the input patterns will also be learned which represent the geometric characteristics of the map types. The characteristics arise from both the structure of individual objects and the topology between objects on a map. Now, with a given map in the SOM, the SOM recognizes the appropriate map type according to the learned input pattern. In addition, one obtains the filenames of the maps as well as the content of the website where the map was found. In the present thesis we also investigated how this additional information can help in the interpretation of map type.
The automatic interpretation of the map scale is a further method in addition to the interpretation of the map objects and map types, which is discussed in the present thesis. The interpretation of the map scale is implemented in two ways: the multi-representation and the details grade. In the former case, the scale of the relevant representation can be derived, where an identical object in different realistic representations on the map is shown; while in the latter case, the scale is derived from the details grade, on the basis of the fact that maps with different scales are displayed on different levels of details.Numéro de notice : 17347 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse étrangère DOI : sans En ligne : http://doi.org/10.18419/opus-3960 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83706 PermalinkLarge scale road network extraction in forested moutainous areas using airborne laser scanning data / António Ferraz (2014)
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PermalinkPanorama de l'intelligence artificielle, ses bases méthodologiques, ses développements, 1. Représentation des connaissances et formalisation des raisonnements / Pierre Marquis (2014)
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PermalinkSemisupervised self-learning for hyperspectral image classification / Immaculada Dopido in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)
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PermalinkAn experimental comparison of semi-supervised learning algorithms for multispectral image classification / Enmei Tu in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 4 (April 2013)
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PermalinkClassification and reconstruction from random projections for hyperspectral imagery / W. Li in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)
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