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Evaluation of automatic prediction of small horizontal curve attributes of mountain roads in GIS environments / Sercan Gülci in ISPRS International journal of geo-information, vol 11 n° 11 (November 2022)
[article]
Titre : Evaluation of automatic prediction of small horizontal curve attributes of mountain roads in GIS environments Type de document : Article/Communication Auteurs : Sercan Gülci, Auteur ; Afiz Hulusi Acar, Auteur ; Abdullah E. Akay, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 560 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de Douglas-Peucker
[Termes IGN] attribut géomètrique
[Termes IGN] coefficient de corrélation
[Termes IGN] courbe
[Termes IGN] matrice de confusion
[Termes IGN] montagne
[Termes IGN] réseau routier
[Termes IGN] système d'information géographique
[Termes IGN] tracé routier
[Termes IGN] Turquie
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Road curve attributes can be determined by using Geographic Information System (GIS) to be used in road vehicle traffic safety and planning studies. This study involves analyzing the GIS-based estimation accuracy in the length, radius and the number of small horizontal road curves on a two-lane rural road and a forest road. The prediction success of horizontal curve attributes was investigated using digitized raw and generalized/simplified road segments. Two different roads were examined, involving 20 test groups and two control groups, using 22 datasets obtained from digitized and surveyed roads based on satellite imagery, GIS estimates, and field measurements. Confusion matrix tables were also used to evaluate the prediction accuracy of horizontal curve geometry. F-score, Mathews Correlation Coefficient, Bookmaker Informedness and Balanced Accuracy were used to investigate the performance of test groups. The Kruskal–Wallis test was used to analyze the statistical relationships between the data. Compared to the Bezier generalization algorithm, the Douglas–Peucker algorithm showed the most accurate horizontal curve predictions at generalization tolerances of 0.8 m and 1 m. The results show that the generalization tolerance level contributes to the prediction accuracy of the number, curve radius, and length of the horizontal curves, which vary with the tolerance value. Thus, this study underlined the importance of calculating generalizations and tolerances following a manual road digitization. Numéro de notice : A2022-847 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11110560 Date de publication en ligne : 09/11/2022 En ligne : https://doi.org/10.3390/ijgi11110560 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102083
in ISPRS International journal of geo-information > vol 11 n° 11 (November 2022) . - n° 560[article]Geographic knowledge graph attribute normalization: Improving the accuracy by fusing optimal granularity clustering and co-occurrence analysis / Chuan Yin in ISPRS International journal of geo-information, vol 11 n° 7 (July 2022)
[article]
Titre : Geographic knowledge graph attribute normalization: Improving the accuracy by fusing optimal granularity clustering and co-occurrence analysis Type de document : Article/Communication Auteurs : Chuan Yin, Auteur ; Binyu Zhang, Auteur ; Wanzeng Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 360 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse de groupement
[Termes IGN] attribut sémantique
[Termes IGN] granularité (informatique)
[Termes IGN] granularité d'image
[Termes IGN] matrice de co-occurrence
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] relation sémantique
[Termes IGN] réseau sémantique
[Termes IGN] synonymieRésumé : (auteur) Expansion of the entity attribute information of geographic knowledge graphs is essentially the fusion of the Internet’s encyclopedic knowledge. However, it lacks structured attribute information, and synonymy and polysemy always exist. These reduce the quality of the knowledge graph and cause incomplete and inaccurate semantic retrieval. Therefore, we normalize the attributes of a geographic knowledge graph based on optimal granularity clustering and co-occurrence analysis, and use structure and the semantic relation of the entity attributes to identify synonymy and correlation between attributes. Specifically: (1) We design a classification system for geographic attributes, that is, using a community discovery algorithm to classify the attribute names. The optimal clustering granularity is identified by the marker target detection algorithm. (2) We complete the fine-grained identification of attribute relations by analyzing co-occurrence relations of the attributes and rule inference. (3) Finally, the performance of the system is verified by manual discrimination using the case of “landscape, forest, field, lake and grass”. The results show the following: (1) The average precision of spatial relations was 0.974 and the average recall was 0.937; the average precision of data relations was 0.977 and the average recall was 0.998. (2) The average F1 for similarity results is 0.473; the average F1 for co-occurrence analysis results is 0.735; the average F1 for rule-based modification results is 0.934; the results show that the accuracy is greater than 90%. Compared to traditional methods only focusing on similarity, the accuracy of synonymous attribute recognition improves the system and we are capable of identifying near-sense attributes. Integration of our system and attribute normalization can greatly improve both the processing efficiency and accuracy. Numéro de notice : A2022-548 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11070360 Date de publication en ligne : 23/06/2022 En ligne : https://doi.org/10.3390/ijgi11070360 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101149
in ISPRS International journal of geo-information > vol 11 n° 7 (July 2022) . - n° 360[article]GisGCN: a visual graph-based framework to match geographical areas through time / Margarita Khokhlova in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)
[article]
Titre : GisGCN: a visual graph-based framework to match geographical areas through time Type de document : Article/Communication Auteurs : Margarita Khokhlova , Auteur ; Nathalie Abadie , Auteur ; Valérie Gouet-Brunet , Auteur ; Liming Chen, Auteur Année de publication : 2022 Projets : Alegoria / Gouet-Brunet, Valérie Article en page(s) : n° 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] attribut géomètrique
[Termes IGN] attribut sémantique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données étiquetées d'entrainement
[Termes IGN] entité géographique
[Termes IGN] image aérienne
[Termes IGN] réseau sémantiqueRésumé : (auteur) Historical visual sources are particularly useful for reconstructing the successive states of the territory in the past and for analysing its evolution. However, finding visual sources covering a given area within a large mass of archives can be very difficult if they are poorly documented. In the case of aerial photographs, most of the time, this task is carried out by solely relying on the visual content of the images. Convolutional Neural Networks are capable to capture the visual cues of the images and match them to each other given a sufficient amount of training data. However, over time and across seasons, the natural and man-made landscapes may evolve, making historical image-based retrieval a challenging task. We want to approach this cross-time aerial indexing and retrieval problem from a different novel point of view: by using geometrical and topological properties of geographic entities of the researched zone encoded as graph representations which are more robust to appearance changes than the pure image-based ones. Geographic entities in the vertical aerial images are thought of as nodes in a graph, linked to each other by edges representing their spatial relationships. To build such graphs, we propose to use instances from topographic vector databases and state-of-the-art spatial analysis methods. We demonstrate how these geospatial graphs can be successfully matched across time by means of the learned graph embedding. Numéro de notice : A2022-156 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11020097 Date de publication en ligne : 29/01/2022 En ligne : https://doi.org/10.3390/ijgi11020097 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100316
in ISPRS International journal of geo-information > vol 11 n° 2 (February 2022) . - n° 97[article]
Titre : Deep learning-based point cloud compression Titre original : Compression de nuages de points par apprentissage profond Type de document : Thèse/HDR Auteurs : Maurice Quach, Auteur ; Frédéric Dufaux, Directeur de thèse ; Giuseppe Valenzise, Directeur de thèse Editeur : Bures-sur-Yvette : Université Paris-Saclay Année de publication : 2022 Importance : 165 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de Doctorat de l'Université de Saclay, spécialité Traitement du signal et des imagesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] attribut
[Termes IGN] compression d'image
[Termes IGN] compression de données
[Termes IGN] géométrie
[Termes IGN] semis de points
[Termes IGN] stockage de donnéesIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Point clouds are becoming essential in key applications with advances in capture technologies leading to large volumes of data.Compression is thus essential for storage and transmission.Point Cloud Compression can be divided into two parts: geometry and attribute compression.In addition, point cloud quality assessment is necessary in order to evaluate point cloud compression methods.Geometry compression, attribute compression and quality assessment form the three main parts of this dissertation.The common challenge across these three problems is the sparsity and irregularity of point clouds.Indeed, while other modalities such as images lie on a regular grid, point cloud geometry can be considered as a sparse binary signal over 3D space and attributes are defined on the geometry which can be both sparse and irregular.First, the state of the art for geometry and attribute compression methods with a focus on deep learning based approaches is reviewed.The challenges faced when compressing geometry and attributes are considered, with an analysis of the current approaches to address them, their limitations and the relations between deep learning and traditional ones.We present our work on geometry compression: a convolutional lossy geometry compression approach with a study on the key performance factors of such methods and a generative model for lossless geometry compression with a multiscale variant addressing its complexity issues.Then, we present a folding-based approach for attribute compression that learns a mapping from the point cloud to a 2D grid in order to reduce point cloud attribute compression to an image compression problem.Furthermore, we propose a differentiable deep perceptual quality metric that can be used to train lossy point cloud geometry compression networks while being well correlated with perceived visual quality and a convolutional neural network for point cloud quality assessment based on a patch extraction approach.Finally, we conclude the dissertation and discuss open questions in point cloud compression, existing solutions and perspectives. We highlight the link between existing point cloud compression research and research problems to relevant areas of adjacent fields, such as rendering in computer graphics, mesh compression and point cloud quality assessment. Note de contenu : 1- Introduction
2- State of the Art on point cloud compression
3- Convolutional neural networks for lossy PCGC
4- Deep generative model for lossless PCGC
5- Deep multiscale lossless PCGC
6- Folding-based PCAC
7- Deep perceptual point cloud quality metric
8- Convolutional Neural Network for PCQANuméro de notice : 24081 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de doctorat : Traitement du signal et des images : Paris-Saclay : 2022 Organisme de stage : Laboratoire des signaux et systèmes DOI : sans En ligne : https://theses.hal.science/tel-03894261 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102331 Evaluating the suitability of multi-scale terrain attribute calculation approaches for seabed mapping applications / Benjamin Misiuk in Marine geodesy, vol 44 n° 4 (July 2021)
[article]
Titre : Evaluating the suitability of multi-scale terrain attribute calculation approaches for seabed mapping applications Type de document : Article/Communication Auteurs : Benjamin Misiuk, Auteur ; Vincent Lecours, Auteur ; M.F.J. Dolan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 327 - 385 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] analyse multiéchelle
[Termes IGN] artefact
[Termes IGN] attribut géomètrique
[Termes IGN] carte bathymétrique
[Termes IGN] cartographie hydrographique
[Termes IGN] fond marin
[Termes IGN] géomorphométrie
[Termes IGN] habitat animal
[Termes IGN] pente
[Termes IGN] réalité de terrain
[Termes IGN] rugosité
[Termes IGN] sondeur multifaisceaux
[Termes IGN] Terre-Neuve, île de (Terre-Neuve-et-Labrador)Résumé : (auteur) The scale dependence of benthic terrain attributes is well-accepted, and multi-scale methods are increasingly applied for benthic habitat mapping. There are, however, multiple ways to calculate terrain attributes at multiple scales, and the suitability of these approaches depends on the purpose of the analysis and data characteristics. There are currently few guidelines establishing the appropriateness of multi-scale raster calculation approaches for specific benthic habitat mapping applications. First, we identify three common purposes for calculating terrain attributes at multiple scales for benthic habitat mapping: (i) characterizing scale-specific terrain features, (ii) reducing data artefacts and errors, and (iii) reducing the mischaracterization of ground-truth data due to inaccurate sample positioning. We then define criteria that calculation approaches should fulfill to address these purposes. At two study sites, five raster terrain attributes, including measures of orientation, relative position, terrain variability, slope, and rugosity were calculated at multiple scales using four approaches to compare the suitability of the approaches for these three purposes. Results suggested that specific calculation approaches were better suited to certain tasks. A transferable parameter, termed the ‘analysis distance’, was necessary to compare attributes calculated using different approaches, and we emphasize the utility of such a parameter for facilitating the generalized comparison of terrain attributes across methods, sites, and scales. Numéro de notice : A2021-526 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/01490419.2021.1925789 Date de publication en ligne : 04/06/2021 En ligne : https://doi.org/10.1080/01490419.2021.1925789 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97967
in Marine geodesy > vol 44 n° 4 (July 2021) . - pp 327 - 385[article]Automating and utilising equal-distribution data classification / Gennady Andrienko in International journal of cartography, vol 7 n° 1 (March 2021)PermalinkConvex hull: another perspective about model predictions and map derivatives from remote sensing data / Jean-Pierre Renaud (2021)PermalinkPerception de scène par un système multi-capteurs, application à la navigation dans des environnements d'intérieur structuré / Marwa Chakroun (2021)PermalinkUsing geometric and semantic attributes for semi-automated tag identification in OpenStreetMap data / Müslüm Hacar (2021)PermalinkVisualization of 3D property data and assessment of the impact of rendering attributes / Stefan Seipel in Journal of Geovisualization and Spatial Analysis, vol 4 n° 2 (December 2020)PermalinkMapping uncertain geographical attributes: incorporating robustness into choropleth classification design / Wangshu Mu in International journal of geographical information science IJGIS, vol 34 n° 11 (November 2020)PermalinkBertin’s forgotten typographic variables and new typographic visualization / Richard Brath in Cartography and Geographic Information Science, vol 46 n° 2 (March 2019)PermalinkAttribute trajectory analysis : a framework to analyse attribute changes using trajectory analysis techniques / Long Zhang in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)PermalinkClassification of aerial photogrammetric 3D point clouds / Carlos Becker in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 5 (mai 2018)PermalinkHarmonic regression of Landsat time series for modeling attributes from national forest inventory data / Barry T. Wilson in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)PermalinkOpen data, big data : quel renouveau du raisonnement cartographique ? / Emilie Lerond in Cartes & Géomatique, n° 235-236 (mars - juin 2018)PermalinkPermalinkAutomatic registration of images to untextured geometry using average shading gradients / Tobias Plötz in International journal of computer vision, vol 125 n° 1-3 (December 2017)PermalinkLearning aggregated features and optimizing model for semantic labeling / Jianhua Wang in The Visual Computer, vol 33 n° 12 (December 2017)PermalinkA geometric correspondence feature based-mismatch removal in vision based-mapping and navigation / Zeyu Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 10 (October 2017)PermalinkA structured regularization framework for spatially smoothing semantic labelings of 3D point clouds / Loïc Landrieu in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)PermalinkSpatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements / Zhibin Ren in Annals of Forest Science, vol 74 n° 3 (September 2017)PermalinkJoint classification and contour extraction of large 3D point clouds / Timo Hackel in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkA novel preunmixing framework for efficient detection of linear mixtures in hyperspectral images / Andrea Marinoni in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkIndex-supported pattern matching on tuples of time-dependent values / Fabio Valdés in Geoinformatica, vol 21 n° 3 (July - September 2017)PermalinkMultivariate label-based thematic maps / Richard Brath in International journal of cartography, vol 3 n° 1 (June 2017)PermalinkA spatial anomaly points and regions detection method using multi-constrained graphs and local density / Yan Shi in Transactions in GIS, vol 21 n° 2 (April 2017)PermalinkAttribute profiles on derived features for urban land cover classification / Bharath Bhushan Damodaran in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)PermalinkEnriched geometric simplification of linear features / Rajesh Tamilmani in Geomatica, vol 71 n° 1 (March 2017)PermalinkContributions méthodologiques pour la caractérisation des milieux par imagerie optique et lidar / Nesrine Chehata (2017)PermalinkSegmentation sémantique de données de télédétection multimodale : application aux peuplements forestiers / Clément Dechesne (2017)PermalinkSegmentation sémantique de peuplements forestiers par analyse conjointe d’imagerie multispectrale très haute résolution et de données 3D Lidar aéroportées / Clément Dechesne (2017)PermalinkTélédétection pour l'observation des surfaces continentales, ch. 6. Méthodes de traitement de données lidar / Clément Mallet (2017)PermalinkDeep filter banks for texture recognition, description, and segmentation / Mircea Cimpoi in International journal of computer vision, vol 118 n° 1 (May 2016)PermalinkGeo-localization using volumetric representations of overhead imagery / Ozge C. Ozcanli in International journal of computer vision, vol 116 n° 3 (February 2016)PermalinkIdentification and utilization of land-use type importance for land-use data generalization / Wenxiu Gao in Cartographic journal (the), Vol 53 n° 1 (February 2016)PermalinkRemote Sensing Observations of Continental Surfaces, ch. 6. Airborne lidar data processing / Clément Mallet (2016)PermalinkSymbolisation et généralisation de données de réseau à différentes échelles / Ha Pham in Cartes & Géomatique, n° 226 (décembre 2015)PermalinkAccurate attribute mapping from volunteered geographic information: issues of volunteer quantity and quality / Giles M. Foody in Cartographic journal (the), Vol 52 n° 4 (November 2015)PermalinkSELF: Semantically Enriched Line simpliFication / Emmanuel Stefanakis in International journal of geographical information science IJGIS, vol 29 n° 10 (October 2015)PermalinkCompilation de données radar et optiques pour la cartographie des classes d'occupation du sol aux environs du système lacustre de Bizerte (Tunisie du Nord) / Ibtissem Amri in Photo interprétation, European journal of applied remote sensing, vol 51 n° 2 (juin 2015)PermalinkVariation du niveau d’abstraction dans le cadre de l’opérateur de zoom sémantique / Géraldine Del Mondo in Revue internationale de géomatique, vol 25 n° 2 (juin - août 2015)PermalinkSpatial interpolation to predict missing attributes in GIS using semantic kriging / Shrutilipi Bhattacharjee in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)PermalinkOut-of-core GPU-based change detection in massive 3D point clouds / Rico Richter in Transactions in GIS, vol 17 n° 5 (October 2013)PermalinkModèle pour un serveur de données géographiques. Les services web géographiques WMS et WFS / Nissrine Souissi in Revue internationale de géomatique, vol 23 n° 2 (juin - aout 2013)PermalinkVers une approche pluridisciplinaire des réseaux enterrés / Lucile Gimenez in XYZ, n° 135 (juin - août 2013)PermalinkComparison of forest attributes derived from two terrestrial lidar systems / Mark J. Ducey in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 3 (March 2013)PermalinkThe annotation process in OpenStreetMap / P. Mooney in Transactions in GIS, vol 16 n° 4 (August 2012)PermalinkA geometry and texture coupled flexible generalization of urban building models / M. Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)PermalinkAutomatic 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)PermalinkClassification orientée-objet supervisée d'une forêt avec une sélection guidée d'attributs personnalisés / Olivier de Joinville in Revue Française de Photogrammétrie et de Télédétection, n° 195 (Novembre 2011)PermalinkLa valeur des données géographiques / Christophe Terrier in Espace géographique, vol 40 n° 2 (avril - juin 2011)PermalinkPostGIS pour les néophytes (3ème partie) : Géométries, création de tables et opérateurs élémentaires / Anonyme in Géomatique expert, n° 79 (01/03/2011)PermalinkPermalinkModeling the scale dependences of topological relations between lines and regions induced by reduction of attributes / S. Du in International journal of geographical information science IJGIS, vol 24 n°11-12 (december 2010)PermalinkUsing clustering methods in geospatial information systems / X. 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Méthodes quantitatives et transformations attributaires / Colette Cauvin (2008)PermalinkQuality aspects in spatial data mining, ch. 5. A multicriteria fusion approach for geographical data matching / Ana-Maria Olteanu-Raimond (2008)PermalinkMatching of 3D surfaces and their intensities / Devrim Akca in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 2 (June 2007)PermalinkAssessing the effect of attribute uncertainty on the robustness of choropleth map classification / N. Xiao in International journal of geographical information science IJGIS, vol 21 n° 1-2 (january 2007)PermalinkDEM resolution dependencies of terrain attributes across a landscape / Y. Deng in International journal of geographical information science IJGIS, vol 21 n° 1-2 (january 2007)PermalinkPermalinkMise en place d'un outil de contrôle de saisie des données vectorielles avec GIS Data Reviewer / N. Rakotobe (2006)PermalinkClassification of spatial properties for spatial allocation modeling / T. Shirabe in Geoinformatica, vol 9 n° 3 (September - November 2005)PermalinkModélisation des erreurs de position et d'attributs dans les bases de données géographiques / Olivier Bonin (2005)PermalinkIntégration dans le SIG GéoConcept de l'outil "Appel police 17" / C. Mzaouiyani (2004)PermalinkInformation géospatiale dans internet : application pour un contexte de renseignement militaire / Marie-Josée Proulx in Revue internationale de géomatique, vol 13 n° 3 (septembre - novembre 2003)PermalinkThe design and implementation of Geographic Information Systems / S.J. Anderson (2003)PermalinkTopographic map generalization: association of road elimination with thematic attributes / Z. Li in Cartographic journal (the), vol 39 n° 2 (December 2002)PermalinkGénéralisation et représentation multiple, ch. 3. La problématique de la représentation multiple / Thomas Devogele (2002)Permalink