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An efficient data organization and scheduling strategy for accelerating large vector data rendering / Mingqiang Guo in Transactions in GIS, vol 21 n° 6 (December 2017)
[article]
Titre : An efficient data organization and scheduling strategy for accelerating large vector data rendering Type de document : Article/Communication Auteurs : Mingqiang Guo, Auteur ; Ying Huang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1217 - 1236 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données vectorielles
[Termes IGN] processeur graphique
[Termes IGN] processeur multicoeur
[Termes IGN] rendu (géovisualisation)
[Termes IGN] traitement parallèle
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Rendering large volumes of vector data is computationally intensive and therefore time consuming, leading to lower efficiency and poorer interactive experience. Graphics processing units (GPUs) are powerful tools in data parallel processing but lie idle most of the time. In this study, we propose an approach to improve the performance of vector data rendering by using the parallel computing capability of many‐core GPUs. Vertex transformation, largely a mathematical calculation that does not require communication with the host storage device, is a time‐consuming procedure because all coordinates of each vector feature need to be transformed to screen vertices. Use of a GPU enables optimization of a general‐purpose mathematical calculation, enabling the procedure to be executed in parallel on a many‐core GPU and optimized effectively. This study mainly focuses on: (1) an organization and storage strategy for vector data based on equal pitch alignment, which can adapt to the GPU's calculating characteristics; (2) a paging‐coalescing transfer and memory access strategy for vector data between the CPU and the GPU; and (3) a balancing allocation strategy to take full advantage of all processing cores of the GPU. Experimental results demonstrate that the approach proposed can significantly improve the efficiency of vector data rendering. Numéro de notice : A2017-837 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12275 Date de publication en ligne : 23/05/2017 En ligne : https://doi.org/10.1111/tgis.12275 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89373
in Transactions in GIS > vol 21 n° 6 (December 2017) . - pp 1217 - 1236[article]Experiments to distribute and parallelize map generalization processes / Guillaume Touya in Cartographic journal (the), Vol 54 n° 4 (November 2017)
[article]
Titre : Experiments to distribute and parallelize map generalization processes Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Justin Berli , Auteur ; Imran Lokhat , Auteur ; Nicolas Regnauld , Auteur Année de publication : 2017 Projets : 1-Pas de projet / Article en page(s) : pp 322 - 332 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] partitionnement
[Termes IGN] traitement parallèle
[Termes IGN] traitement réparti
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) Automatic map generalization requires the use of computationally intensive processes often unable to deal with large datasets. Distributing the generalization process is the only way to make them scalable and usable in practice. But map generalization is a highly contextual process, and the surroundings of a generalized map feature needs to be known to generalize the feature, which is a problem as distribution might partition the dataset and parallelize the processing of each part. This paper proposes experiments to evaluate the past propositions to distribute map generalization, and to identify the main remaining issues. The past propositions to distribute map generalization are first discussed, and then the experiment hypotheses and apparatus are described. The experiments confirmed that regular partitioning was the quickest strategy, but less effective when taking context into account. The geographical partitioning, though less effective for now, is quite promising regarding the quality of the results as it better integrates the geographical context. Numéro de notice : A2017-827 Affiliation des auteurs : LASTIG COGIT (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00087041.2017.1413787 Date de publication en ligne : 19/02/2018 En ligne : https://doi.org/10.1080/00087041.2017.1413787 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89359
in Cartographic journal (the) > Vol 54 n° 4 (November 2017) . - pp 322 - 332[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 030-2017041 RAB Revue Centre de documentation En réserve L003 Disponible Fusion of hyperspectral and LiDAR data using sparse and low-rank component analysis / Behnood Rasti in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)
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Titre : Fusion of hyperspectral and LiDAR data using sparse and low-rank component analysis Type de document : Article/Communication Auteurs : Behnood Rasti, Auteur ; Pedram Ghamisi, Auteur ; Javier Plaza, Auteur Année de publication : 2017 Article en page(s) : pp 6354 - 6365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse en composantes principales
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fusion de données
[Termes IGN] Houston (Texas)
[Termes IGN] image hyperspectrale
[Termes IGN] TrenteRésumé : (Auteur) The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank technique is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features. The proposed fusion technique consists of two main steps. First, extinction profiles are used to extract spatial and elevation information from hyperspectral and LiDAR data, respectively. Then, the sparse and low-rank technique is utilized to estimate the low-rank fused features from the extracted ones that are eventually used to produce a final classification map. The proposed approach is evaluated over an urban data set captured over Houston, USA, and a rural one captured over Trento, Italy. Experimental results confirm that the proposed fusion technique outperforms the other techniques used in the experiments based on the classification accuracies obtained by random forest and support vector machine classifiers. Moreover, the proposed approach can effectively classify joint LiDAR and hyperspectral data in an ill-posed situation when only a limited number of training samples are available. Numéro de notice : A2017-748 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2726901 En ligne : https://doi.org/10.1109/TGRS.2017.2726901 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88783
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6354 - 6365[article]Salient object detection in complex scenes via D-S evidence theory based region classification / Chunlei Yang in The Visual Computer, vol 33 n° 11 (November 2017)
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Titre : Salient object detection in complex scenes via D-S evidence theory based region classification Type de document : Article/Communication Auteurs : Chunlei Yang, Auteur ; Jiexin Pu, Auteur ; Yongsheng Dong, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1415 - 1428 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] fusion de données
[Termes IGN] information complexe
[Termes IGN] scène intérieure
[Termes IGN] segmentation d'image
[Termes IGN] théorie de Dempster-Shafer
[Termes IGN] zone saillante 3DRésumé : (Auteur) In complex scenes, multiple objects are often concealed in cluttered backgrounds. Their saliency is difficult to be detected by using conventional methods, mainly because single color contrast can not shoulder the mission of saliency measure; other image features should be involved in saliency detection to obtain more accurate results. Using Dempster-Shafer (D-S) evidence theory based region classification, a novel method is presented in this paper. In the proposed framework, depth feature information extracted from a coarse map is employed to generate initial feature evidences which indicate the probabilities of regions belonging to foreground or background. Based on the D-S evidence theory, both uncertainty and imprecision are modeled, and the conflicts between different feature evidences are properly resolved. Moreover, the method can automatically determine the mass functions of the two-stage evidence fusion for region classification. According to the classification result and region relevance, a more precise saliency map can then be generated by manifold ranking. To further improve the detection results, a guided filter is utilized to optimize the saliency map. Both qualitative and quantitative evaluations on three publicly challenging benchmark datasets demonstrate that the proposed method outperforms the contrast state-of-the-art methods, especially for detection in complex scenes. Numéro de notice : A2017-713 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-016-1288-y En ligne : https://doi.org/10.1007/s00371-016-1288-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88094
in The Visual Computer > vol 33 n° 11 (November 2017) . - pp 1415 - 1428[article]Partial polygon pruning of hydrographic features in automated generalization / Alexander K. Stum in Transactions in GIS, vol 21 n° 5 (October 2017)
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Titre : Partial polygon pruning of hydrographic features in automated generalization Type de document : Article/Communication Auteurs : Alexander K. Stum, Auteur ; Barbara P. Buttenfield, Auteur ; Lauwrence V. Stanislawski, Auteur Année de publication : 2017 Article en page(s) : pp 1061–1078 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] base de données hydrographiques
[Termes IGN] détection automatique
[Termes IGN] Etats-Unis
[Termes IGN] généralisation automatique de données
[Termes IGN] petite échelle
[Termes IGN] polygone
[Termes IGN] rendu (géovisualisation)
[Termes IGN] simplification de contour
[Termes IGN] traitement automatique de données
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) This article demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi-scale hydrographic database. Water features are sensitive to scale change, therefore multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal features – such as long, sinuous reservoir arms, stream channels too narrow at the target scale, and islands that begin to coalesce – entails concurrent management of the length and width of polygonal features as well as integrating pruned polygons with other generalized point and linear hydrographic features to maintain stream network connectivity. The implementation follows data representation standards developed by the US Geological Survey (USGS) for the National Hydrography Dataset (NHD). Portions of polygonal rivers, streams, and canals are automatically characterized for width, length, and connectivity. This article describes an algorithm for automatic detection and subsequent processing, and shows results for a sample of NHD subbasins in different landscape conditions in the US. Numéro de notice : A2017-634 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12270 En ligne : http://dx.doi.org/10.1111/tgis.12270 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86953
in Transactions in GIS > vol 21 n° 5 (October 2017) . - pp 1061–1078[article]Uncertain Voronoi cell computation based on space decomposition / Klaus Arthur Schmid in Geoinformatica, vol 21 n° 4 (October - December 2017)PermalinkAn information fusion approach for PALSAR data to retrieve soil moisture / Ankita Jain in Geocarto international, vol 32 n° 9 (September 2017)PermalinkA new GPU bundle adjustment method for large-scale data / Zhou Shunping ; Xiong Xiaodong ; Junfeng Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 9 (September 2017)PermalinkDu travail de pro ! / Benoît Greuzat in Géomètre, n° 2150 (septembre 2017)PermalinkA TV prior for high-quality scalable multi-view stereo reconstruction / Andreas Kuhn in International journal of computer vision, vol 124 n° 1 (August 2017)PermalinkAutomation of point cloud processing to increase the deformation monitoring accuracy / Ján Erdélyi in Applied geomatics, vol 9 n° 2 (June 2017)PermalinkDéveloppement du processus de modélisation 3D de maquettes numériques à partir de nuages de points / Thibault Bavoux in XYZ, n° 151 (juin - août 2017)PermalinkA parallel scheme for large-scale polygon rasterization on CUDA-enabled GPUs / Chen Zhou in Transactions in GIS, vol 21 n° 3 (June 2017)PermalinkDesign principles of a stream-based framework for mobility analysis / Loic Salmon in Geoinformatica, vol 21 n° 2 (April - June 2017)PermalinkAn effective approach to estimating computing time of vector data spatial computational domains in WebGIS / Mingqiang Guo in Geomatica, vol 71 n° 1 (March 2017)PermalinkVol 44 n° 2 - March 2017 - Crowdsourced Mapping (Bulletin de Cartography and Geographic Information Science)PermalinkOn the fusion of lidar and aerial color imagery to detect urban vegetation and buildings / Madhurima Bandyopadhyay in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 2 (February 2017)PermalinkCombination of image descriptors for the exploration of cultural photographic collections / Neelanjan Bhowmik in Journal of Electronic Imaging, vol 26 n° 1 (January - February 2017)PermalinkContributions méthodologiques pour la caractérisation des milieux par imagerie optique et lidar / Nesrine Chehata (2017)PermalinkDes données à l'information / Florent Chavand (2017)PermalinkFusion of graph embedding and sparse representation for feature extraction and classification of hyperspectral imagery / Fulin Luo in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 1 (January 2017)PermalinkHandbook on advances in remote sensing and geographic information systems / Margarita N. Favorskaya (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)PermalinkAn integrated framework for the spatio–temporal–spectral fusion of remote sensing images / Huanfeng Shen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)Permalink