Descripteur
Documents disponibles dans cette catégorie (180)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
![Tris disponibles](./images/orderby_az.gif)
Etendre la recherche sur niveau(x) vers le bas
A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data / Guiming Zhang in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)
![]()
[article]
Titre : A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data Type de document : Article/Communication Auteurs : Guiming Zhang, Auteur ; A - Xing Zhu, Auteur ; Qunying Huang, Auteur Année de publication : 2017 Article en page(s) : pp 2068 - 2097 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] données massives
[Termes IGN] estimation par noyau
[Termes IGN] jeu de données localisées
[Termes IGN] optimisation (mathématiques)
[Termes IGN] processeur graphiqueRésumé : (Auteur) Kernel density estimation (KDE) is a classic approach for spatial point pattern analysis. In many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over KDE with an invariant bandwidth (fixed KDE). However, bandwidths determination for adaptive KDE is extremely computationally intensive, particularly for point pattern analysis tasks of large problem sizes. This computational challenge impedes the application of adaptive KDE to analyze large point data sets, which are common in this big data era. This article presents a graphics processing units (GPUs)-accelerated adaptive KDE algorithm for efficient spatial point pattern analysis on spatial big data. First, optimizations were designed to reduce the algorithmic complexity of the bandwidth determination algorithm for adaptive KDE. The massively parallel computing resources on GPU were then exploited to further speed up the optimized algorithm. Experimental results demonstrated that the proposed optimizations effectively improved the performance by a factor of tens. Compared to the sequential algorithm and an Open Multiprocessing (OpenMP)-based algorithm leveraging multiple central processing unit cores for adaptive KDE, the GPU-enabled algorithm accelerated point pattern analysis tasks by a factor of hundreds and tens, respectively. Additionally, the GPU-accelerated adaptive KDE algorithm scales reasonably well while increasing the size of data sets. Given the significant acceleration brought by the GPU-enabled adaptive KDE algorithm, point pattern analysis with the adaptive KDE approach on large point data sets can be performed efficiently. Point pattern analysis on spatial big data, computationally prohibitive with the sequential algorithm, can be conducted routinely with the GPU-accelerated algorithm. The GPU-accelerated adaptive KDE approach contributes to the geospatial computational toolbox that facilitates geographic knowledge discovery from spatial big data. Numéro de notice : A2017-509 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1324975 En ligne : http://dx.doi.org/10.1080/13658816.2017.1324975 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86455
in International journal of geographical information science IJGIS > vol 31 n° 9-10 (September - October 2017) . - pp 2068 - 2097[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2017051 RAB Revue Centre de documentation En réserve L003 Disponible An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings / Sergio J. Rey in Transactions in GIS, vol 21 n° 4 (August 2017)
![]()
[article]
Titre : An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings Type de document : Article/Communication Auteurs : Sergio J. Rey, Auteur ; Philip Stephens, Auteur ; Jason Laura, Auteur Année de publication : 2017 Article en page(s) : pp 796 - 810 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] autocorrélation spatiale
[Termes IGN] carte choroplèthe
[Termes IGN] classification contextuelle
[Termes IGN] données massives
[Termes IGN] échantillon
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] précision de la classification
[Termes IGN] simulation
[Termes IGN] traitement de données localiséesRésumé : (Auteur) Large data contexts present a number of challenges to optimal choropleth map classifiers. Application of optimal classifiers to a sample of the attribute space is one proposed solution. The properties of map classifiers methods are examined through a series of Monte Carlo simulations. The impacts of spatial autocorrelation, number of desired classes, and form of sampling are shown to have significant impacts on the accuracy of map classifications. Tradeoffs between improved speed of the sampling approaches and loss of accuracy are also considered. The results suggest the possibility of guiding the choice of classification scheme as a function of the properties of large data sets. Numéro de notice : A2017-630 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12236 En ligne : http://dx.doi.org/10.1111/tgis.12236 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86945
in Transactions in GIS > vol 21 n° 4 (August 2017) . - pp 796 - 810[article]Geospatial big data and archaeology: Prospects and problems too great to ignore / Mark D. McCoy in Journal of archaeological science, vol 84 (August 2017)
![]()
[article]
Titre : Geospatial big data and archaeology: Prospects and problems too great to ignore Type de document : Article/Communication Auteurs : Mark D. McCoy, Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] archéologie
[Termes IGN] archéométrie
[Termes IGN] données localisées
[Termes IGN] données massives
[Termes IGN] métadonnées
[Termes IGN] Nouvelle-Zélande
[Termes IGN] protection de la vie privée
[Termes IGN] qualité des donnéesRésumé : (auteur) As spatial technology has evolved and become integrated in to archaeology, we face a new set of challenges posed by the sheer size and complexity of data we use and produce. In this paper I discuss the prospects and problems of Geospatial Big Data (GBD) – broadly defined as data sets with locational information that exceed the capacity of widely available hardware, software, and/or human resources. While the datasets we create today remain within available resources, we nonetheless face the same challenges as many other fields that use and create GBD, especially in apprehensions over data quality and privacy. After reviewing the kinds of archaeological geospatial data currently available I discuss the near future of GBD in writing culture histories, making decisions, and visualizing the past. I use a case study from New Zealand to argue for the value of taking a data quantity-in-use approach to GBD and requiring applications of GBD in archaeology be regularly accompanied by a Standalone Quality Report. Numéro de notice : A2017-387 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.jas.2017.06.003 En ligne : https://doi.org/10.1016/j.jas.2017.06.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85816
in Journal of archaeological science > vol 84 (August 2017)[article]Joint classification and contour extraction of large 3D point clouds / Timo Hackel in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
![]()
[article]
Titre : Joint classification and contour extraction of large 3D point clouds Type de document : Article/Communication Auteurs : Timo Hackel, Auteur ; Jan Dirk Wegner, Auteur ; Konrad Schindler, Auteur Année de publication : 2017 Article en page(s) : pp 231 - 245 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] attribut sémantique
[Termes IGN] classification dirigée
[Termes IGN] compréhension de l'image
[Termes IGN] densité des points
[Termes IGN] détection de contours
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données massives
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Auteur) We present an effective and efficient method for point-wise semantic classification and extraction of object contours of large-scale 3D point clouds. What makes point cloud interpretation challenging is the sheer size of several millions of points per scan and the non-grid, sparse, and uneven distribution of points. Standard image processing tools like texture filters, for example, cannot handle such data efficiently, which calls for dedicated point cloud labeling methods. It turns out that one of the major drivers for efficient computation and handling of strong variations in point density, is a careful formulation of per-point neighborhoods at multiple scales. This allows, both, to define an expressive feature set and to extract topologically meaningful object contours.
Semantic classification and contour extraction are interlaced problems. Point-wise semantic classification enables extracting a meaningful candidate set of contour points while contours help generating a rich feature representation that benefits point-wise classification. These methods are tailored to have fast run time and small memory footprint for processing large-scale, unstructured, and inhomogeneous point clouds, while still achieving high classification accuracy. We evaluate our methods on the semantic3d.net benchmark for terrestrial laser scans with
points.Numéro de notice : A2017-515 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.05.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.05.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86476
in ISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 231 - 245[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt PolarGlobe : A web-wide virtual globe system for visualizing multidimensional, time-varying, big climate data / Wenwen Li in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)
![]()
[article]
Titre : PolarGlobe : A web-wide virtual globe system for visualizing multidimensional, time-varying, big climate data Type de document : Article/Communication Auteurs : Wenwen Li, Auteur ; Sizhe Wang, Auteur Année de publication : 2017 Article en page(s) : pp 1562 - 1582 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Arctique
[Termes IGN] changement climatique
[Termes IGN] cyberinfrastructure
[Termes IGN] données massives
[Termes IGN] données multidimensionnelles
[Termes IGN] expérience scientifique
[Termes IGN] géovisualisation
[Termes IGN] globe virtuel
[Termes IGN] image multitemporelle
[Termes IGN] prototype
[Termes IGN] rendu (géovisualisation)
[Termes IGN] webGL
[Vedettes matières IGN] GéovisualisationRésumé : (Auteur) The increasing research interest in global climate change and the rise of the public awareness have generated a significant demand for new tools to support effective visualization of big climate data in a cyber environment such that anyone from any location with an Internet connection and a web browser can easily view and comprehend the data. In response to the demand, this paper introduces a new web-based platform for visualizing multidimensional, time-varying climate data on a virtual globe. The web-based platform is built upon a virtual globe system Cesium, which is open-source, highly extendable and capable of being easily integrated into a web environment. The emerging WebGL technique is adapted to support interactive rendering of 3D graphics with hardware graphics acceleration. To address the challenges of transmitting and visualizing voluminous, complex climate data over the Internet to support real-time visualization, we develop a stream encoding and transmission strategy based on video-compression techniques. This strategy allows dynamic provision of scientific data in different precisions to balance the needs for scientific analysis and visualization cost. Approaches to represent, encode and decode processed data are also introduced in detail to show the operational workflow. Finally, we conduct several experiments to demonstrate the performance of the proposed strategy under different network conditions. A prototype, PolarGlobe, has been developed to visualize climate data in the Arctic regions from multiple angles. Numéro de notice : A2017-312 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1306863 En ligne : http://dx.doi.org/10.1080/13658816.2017.1306863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85366
in International journal of geographical information science IJGIS > vol 31 n° 7-8 (July - August 2017) . - pp 1562 - 1582[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2017041 RAB Revue Centre de documentation En réserve L003 Disponible 079-2017042 RAB Revue Centre de documentation En réserve L003 Disponible A parallel scheme for large-scale polygon rasterization on CUDA-enabled GPUs / Chen Zhou in Transactions in GIS, vol 21 n° 3 (June 2017)
PermalinkGeospatial big data and cartography : research challenges and opportunities for making maps that matter / Anthony C. Robinson in International journal of cartography, vol 3 suppl 1 (May 2017)
PermalinkMapping fine-scale population distributions at the building level by integrating multisource geospatial big data / Yao Yao in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)
PermalinkA scalable and multi-purpose point cloud server (PCS) for easier and faster point cloud data management and processing / Rémi Cura in ISPRS Journal of photogrammetry and remote sensing, vol 127 (May 2017)
PermalinkSpatial query based virtual reality GIS analysis platform / Weixi Wang in Neurocomputing, vol (2017)
PermalinkDistributed processing of big mobility data as spatio-temporal data streams / Zdravko Galić in Geoinformatica, vol 21 n° 2 (April - June 2017)
PermalinkLe travail de la science et le numérique : Données, publications, plateformes / Direction de l'information scientifique et technique - CNRS (20/02/2017)
PermalinkPermalinkPermalinkvol 43 n° 5 - November 2016 - Integrating big social data, computing and modeling for spatial social science (Bulletin de Cartography and Geographic Information Science) / Xinyue Ye
Permalink