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Assessing the performance of multi-GNSS precise point positioning in Asia-Pacific region / X. Zhao in Survey review, vol 49 n° 354 (September 2017)
[article]
Titre : Assessing the performance of multi-GNSS precise point positioning in Asia-Pacific region Type de document : Article/Communication Auteurs : X. Zhao, Auteur ; S. Wang, Auteur ; C. Liu, Auteur ; J. Ou, Auteur ; X. Yu, Auteur Année de publication : 2017 Article en page(s) : pp 186 - 196 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] Asie (géographie politique)
[Termes IGN] données BeiDou
[Termes IGN] données GLONASS
[Termes IGN] données GPS
[Termes IGN] filtre de Kalman
[Termes IGN] modèle stochastique
[Termes IGN] Pacifique (océan)
[Termes IGN] positionnement ponctuel précis
[Termes IGN] test de performanceRésumé : (Auteur) Multi-Global Navigation Satellite System (GNSS) integration can effectively improve the satellite geometry strength, and certain effect on the precise point positioning (PPP) accuracy and convergence speed. Taking the system difference into account, this paper deduces the unified GNSS observation model, which is extended to the multi-GNSS PPP functional model. Meanwhile, the stochastic model used in Kalman filter parameter estimation is presented in the paper. Furthermore, to evaluate the performance of the multi-GNSS PPP in Asia-Pacific region, observed data from the International GNSS Service reference stations are analysed using the self-developed software. In detail, the results from Global Positioning System (GPS)-, GLObal NAvigation Satellite System (GLONASS)- and BeiDou Navigation Satellite System (BDS)-only PPP, double combined PPP and GPS/GLONASS/BDS combined PPP under the different observation duration and cut-off elevation angles are analysed. Results demonstrate: (1) compared with the single system PPP, the convergence speed of the multi-GNSS PPP is improved while the accuracy is not significantly improved after processing the 24-h data set; (2) when the observation duration is short, such as 0.5 h, the mean convergence percentage of the BDS combined with GPS and GLONASS PPP increases by an average of 49.6% compared with the single individual systems except for BDS, respectively, under the cut-off angle of 5° and (3) when PPP positioning with high cut-off elevation angles, and at the point of centimetre-level positioning, the GPS/GLONASS/BDS combined PPP has a better performance on the convergence percentage and convergence speed. For example, the percentages of the position biases within 0–5 cm for GPS/GLONASS/BDS are increased by 7.2 and 4.5% in North and East direction compared with GPS/GLONASS under the cut-off angle of 35°, more than any other. And the mean convergence time is only 14.5 min. Numéro de notice : A2017-544 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/00396265.2016.1151576 En ligne : https://doi.org/10.1080/00396265.2016.1151576 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86599
in Survey review > vol 49 n° 354 (September 2017) . - pp 186 - 196[article]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]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2017051 RAB Revue Centre de documentation En réserve L003 Disponible Impact of spatial correlations on the surface estimation based on terrestrial laser scanning / Tobias Jurek in Journal of applied geodesy, vol 11 n° 3 (September 2017)
[article]
Titre : Impact of spatial correlations on the surface estimation based on terrestrial laser scanning Type de document : Article/Communication Auteurs : Tobias Jurek, Auteur ; Heiner Kuhlmann, Auteur ; Christoph Holst, Auteur Année de publication : 2017 Article en page(s) : pp 143 - 156 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] déformation géométrique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation statistique
[Termes IGN] modèle stochastiqueRésumé : (Auteur) In terms of high precision requested deformation analyses, evaluating laser scan data requires the exact knowledge of the functional and stochastic model. If this is not given, a parameter estimation leads to insufficient results. Simulating a laser scanning scene provides the knowledge of the exact functional model of the surface. Thus, it is possible to investigate the impact of neglecting spatial correlations in the stochastic model. Here, this impact is quantified through statistical analysis.
The correlation function, the number of scanning points and the ratio of colored noise in the measurements determine the covariances in the simulated observations. It is shown that even for short correlation lengths of less than 10 cm and a low ratio of colored noise the global test as well as the parameter test are rejected. This indicates a bias and inconsistency in the parameter estimation. These results are transferable to similar tasks of laser scanner based surface approximation.Numéro de notice : A2017-569 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1515/jag-2017-0006 En ligne : https://doi.org/10.1515/jag-2017-0006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86689
in Journal of applied geodesy > vol 11 n° 3 (September 2017) . - pp 143 - 156[article]A Markov chain model for simulating wood supply from any-aged forest management based on national forest inventory (NFI) data / Jari Vauhkonen in Forests, vol 8 n° 9 (September 2017)
[article]
Titre : A Markov chain model for simulating wood supply from any-aged forest management based on national forest inventory (NFI) data Type de document : Article/Communication Auteurs : Jari Vauhkonen, Auteur ; Tuula Packalen, Auteur Année de publication : 2017 Article en page(s) : pp 307 - Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] chaîne de Markov
[Termes IGN] Finlande
[Termes IGN] gestion forestière durable
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de simulation
[Termes IGN] modélisation de la forêt
[Termes IGN] ressources forestières
[Vedettes matières IGN] Economie forestièreRésumé : (auteur) Markov chain models have been applied for a long time to simulate forest dynamics based on transitions in matrices of tree diameter classes or areas of forest size and structure types. To date, area-based matrix models have been applied assuming either even-aged or uneven-aged forest management. However, both management systems may be applied simultaneously due to land-use constraints or the rationality of combining the systems, which is called any-aged management. We integrated two different Markov chain models, one for even-aged and another for uneven-aged forest management, in an area-based approach to analyze wood supply from any-aged forest management. We evaluate the impacts of parameterizing the model based on available data sets, namely permanent and temporary Finnish National Forest Inventory (NFI) sample plots and a plot-level simulator to determine transitions due to different types of thinning treatments, and present recommendations for the related methodological choices. Our overall observation is that the combined modelling chain simulated the development of both the even- and uneven-aged forest structures realistically. Due to the flexibility of the implementation, the approach is very well suited for situations where scenario assumptions need to be varied according to expected changes in silvicultural practices or land-use constraints, for example. Numéro de notice : A2017-636 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f8090307 En ligne : http://doi.org/10.3390/f8090307 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86986
in Forests > vol 8 n° 9 (September 2017) . - pp 307 -[article]3D local feature BKD to extract road information from mobile laser scanning point clouds / Yang Bisheng in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
[article]
Titre : 3D local feature BKD to extract road information from mobile laser scanning point clouds Type de document : Article/Communication Auteurs : Yang Bisheng, Auteur ; Yuan Liu, Auteur ; Zhen Dong, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 329 - 343 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classificateur
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] densité des points
[Termes IGN] données localisées 3D
[Termes IGN] estimation par noyau
[Termes IGN] extraction du réseau routier
[Termes IGN] semis de points
[Termes IGN] télémétrie laser mobile
[Termes IGN] variable binaireRésumé : (Auteur) Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. Numéro de notice : A2017-517 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.06.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.06.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86479
in ISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 329 - 343[article]Réservation
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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 An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings / Sergio J. 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