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From Google Maps to a fine-grained catalog of street trees / Steve Branson in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)
[article]
Titre : From Google Maps to a fine-grained catalog of street trees Type de document : Article/Communication Auteurs : Steve Branson, Auteur ; Jan Dirk Wegner, Auteur ; David Hall, Auteur ; Nico Lang, Auteur ; Konrad Schindler, Auteur ; Pietro Perona, Auteur Année de publication : 2018 Article en page(s) : pp 13 - 30 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] arbre urbain
[Termes IGN] architecture pipeline (processeur)
[Termes IGN] classification dirigée
[Termes IGN] détection de changement
[Termes IGN] Google Maps
[Termes IGN] inventaire de la végétation
[Termes IGN] photo-interprétation assistée par ordinateur
[Termes IGN] réseau neuronal convolutif
[Termes IGN] villeRésumé : (Auteur) Up-to-date catalogs of the urban tree population are of importance for municipalities to monitor and improve quality of life in cities. Despite much research on automation of tree mapping, mainly relying on dedicated airborne LiDAR or hyperspectral campaigns, tree detection and species recognition is still mostly done manually in practice. We present a fully automated tree detection and species recognition pipeline that can process thousands of trees within a few hours using publicly available aerial and street view images of Google MapsTM. These data provide rich information from different viewpoints and at different scales from global tree shapes to bark textures. Our work-flow is built around a supervised classification that automatically learns the most discriminative features from thousands of trees and corresponding, publicly available tree inventory data. In addition, we introduce a change tracker that recognizes changes of individual trees at city-scale, which is essential to keep an urban tree inventory up-to-date. The system takes street-level images of the same tree location at two different times and classifies the type of change (e.g., tree has been removed). Drawing on recent advances in computer vision and machine learning, we apply convolutional neural networks (CNN) for all classification tasks. We propose the following pipeline: download all available panoramas and overhead images of an area of interest, detect trees per image and combine multi-view detections in a probabilistic framework, adding prior knowledge; recognize fine-grained species of detected trees. In a later, separate module, track trees over time, detect significant changes and classify the type of change. We believe this is the first work to exploit publicly available image data for city-scale street tree detection, species recognition and change tracking, exhaustively over several square kilometers, respectively many thousands of trees. Experiments in the city of Pasadena, California, USA show that we can detect >70% of the street trees, assign correct species to >80% for 40 different species, and correctly detect and classify changes in >90% of the cases. Numéro de notice : A2018-068 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.008 Date de publication en ligne : 20/11/2017 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.11.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89426
in ISPRS Journal of photogrammetry and remote sensing > vol 135 (January 2018) . - pp 13 - 30[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018011 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018012 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2018013 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt
Titre : Machine learning - advanced techniques and emerging applications Type de document : Monographie Auteurs : Hamed Farhadi, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2018 Importance : 230 p. Format : 19 x 27 cm ISBN/ISSN/EAN : 9781789237528 9781789237535 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] données massives
[Termes IGN] informatique en nuage
[Termes IGN] processeur graphique
[Termes IGN] statistiquesRésumé : (éditeur) The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses. Note de contenu : 1- Hardware accelerator design for machine learning
2- Regression models to predict air pollution from affordable data collections
3- Multiple kernel-based multimedia fusion for automated event detection from tweets
4- Using sentiment analysis and machine learning algorithms to determine citizens’ perceptions
5- Overcoming challenges in predictive modeling of Laser-plasma interaction scenarios. The sinuous route from advanced machine learning to deep learning
6- Machine learning approaches for spectrum management in cognitive radio networks
7- Machine learning algorithm for wireless indoor localization
8- classification of malaria-infected cells using deep convolutional neuronal networks
9- Machine learning in educational technology
10- Sentiment-based semantic rule learning for improved product recommandations
11- A multilevel evolutionary algorithm applied to the maximum satisfiability problemsNuméro de notice : 25952 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.69783 En ligne : https://doi.org/10.5772/intechopen.69783 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96406 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]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 A 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)
[article]
Titre : A new GPU bundle adjustment method for large-scale data Type de document : Article/Communication Auteurs : Zhou Shunping, Auteur ; Xiong Xiaodong, Auteur ; Junfeng Zhu, Auteur Année de publication : 2017 Article en page(s) : pp 633 - 641 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] compensation par faisceaux
[Termes IGN] jeu de données
[Termes IGN] méthode du gradient conjugué
[Termes IGN] processeur graphique
[Termes IGN] traitement parallèleRésumé : (Auteur) We developed a fast and effective bundle adjustment method for large-scale datasets. The preconditioned conjugate gradient (PCG) algorithm and GPU parallel computing technology are simultaneously applied to deal with large-scale data and to accelerate the bundle adjustment process. The whole bundle adjustment process is modified to enable parallel computing. The critical optimization on parallel task assignment and GPU memory usage are specified. The proposed method was tested using 10 datasets. The traditional Levenberg Marquardt (LM) method, advanced PCG method, Wu's method and the proposed GPU parallel computing method are all compared and analyzed. Preliminary results have shown that the proposed method can process a large-scale dataset with about 13,000 images in less than three minutes on a common computer with GPU device. The efficiency of the proposed method is about the same with Wu's method while the accuracy is better. Numéro de notice : A2017-609 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.9.633 En ligne : https://doi.org/10.14358/PERS.83.9.633 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86887
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 9 (September 2017) . - pp 633 - 641[article]A parallel scheme for large-scale polygon rasterization on CUDA-enabled GPUs / Chen Zhou in Transactions in GIS, vol 21 n° 3 (June 2017)PermalinkThe index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing / Lihong Su in Geoinformatica, vol 20 n° 4 (October - December 2016)PermalinkA simulated annealing algorithm for zoning in planning using parallel computing / Inès Santé in Computers, Environment and Urban Systems, vol 59 (September 2016)PermalinkSpaceborne synthetic aperture radar data focusing on multicore-based architectures / Pasquale Imperatore in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)PermalinkA bootstrap test for constant coefficients in geographically weighted regression models / Chang-Lin Mei in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)PermalinkEfficient visualization of urban simulation data using modern GPUs / Aleksandr Zagarskikh in Procedia Computer Science, vol 51 (2015)PermalinkParallel performance of typical algorithms in remote sensing-based mapping on a multi-core computer / Jinghui Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 5 (May 2015)PermalinkExtracting mobile objects in images using a Velodyne lidar point cloud / Bruno Vallet in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W4 (March 2015)PermalinkProvenance capture and use in a satellite data processing pipeline / Scott Jensen in IEEE Transactions on geoscience and remote sensing, vol 51 n° 11 (November 2013)PermalinkOut-of-core GPU-based change detection in massive 3D point clouds / Rico Richter in Transactions in GIS, vol 17 n° 5 (October 2013)Permalink