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vol 117 - July 2016 - Multi-dimensional modeling, analysis and visualization (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / Eric Guilbert  

Public [n° ou bulletin]  est un bulletin de ISPRS Journal of photogrammetry and remote sensing / International society for photogrammetry and remote sensing (1980 -) (1990 -)   Titre : vol 117 - July 2016 - Multi-dimensional modeling, analysis and visualization Type de document : Périodique Auteurs : Eric Guilbert, Éditeur scientifique ; Arzu Çöltekin, Éditeur scientifique ; Francesc Antón Castro, Éditeur scientifique ; Christopher Pettit, Éditeur scientifique Année de publication : 2016 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse spatiale [Termes IGN] modélisation spatiale [Termes IGN] visualisation Numéro de notice : 081-201607 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Numéro de périodique En ligne : http://www.sciencedirect.com/science/journal/09242716/117 Format de la ressource électronique : URL sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=27172 [n° ou bulletin]

Contient

• A web-based 3D visualisation and assessment system for urban precinct scenario modelling / Roman Trubka in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)  
• Self-calibration of digital aerial camera using combined orthogonal models / Hadi Babapour in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)  
• Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas / Xiaoqian Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)  
• A new adaptive method to filter terrestrial laser scanner point clouds using morphological filters and spectral information to conserve surface micro-topography / Emilio Rodríguez-Caballero in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)  
• A general variational framework considering cast shadows for the topographic correction of remote sensing imagery / Huifang Li in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)  
• The story of DB4GeO – A service-based geo-database architecture to support multi-dimensional data analysis and visualization / Martin Breunig in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)  
• Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series / Meng Lu in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)  

Geospatial big data handling theory and methods: A review and research challenges / Songnian Li in ISPRS Journal of photogrammetry and remote sensing, vol 115 (May 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 115 (May 2016) . - pp 119 – 133 Titre : Geospatial big data handling theory and methods: A review and research challenges Type de document : Article/Communication Auteurs : Songnian Li, Auteur ; Suzana Dragićević, Auteur ; Francesc Antón Castro, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 119 – 133 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] données massives [Termes IGN] traitement automatique de données [Termes IGN] traitement de données localisées Résumé : (auteur) Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future. Numéro de notice : A2016-547 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.10.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.10.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81699 [article]

Classified and clustered data constellation: An efficient approach of 3D urban data management / Suhaibah Azri in ISPRS Journal of photogrammetry and remote sensing, vol 113 (March 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 113 (March 2016) . - pp 30 - 42 Titre : Classified and clustered data constellation: An efficient approach of 3D urban data management Type de document : Article/Communication Auteurs : Suhaibah Azri, Auteur ; Uznir Ujang, Auteur ; Francesc Antón Castro, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 30 - 42 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de groupement [Termes IGN] base de données [Termes IGN] classification dirigée [Termes IGN] données massives [Termes IGN] exploration de données [Termes IGN] gestion urbaine [Termes IGN] milieu urbain [Termes IGN] noeud [Termes IGN] recherche d'information géographique Résumé : (auteur) The growth of urban areas has resulted in massive urban datasets and difficulties handling and managing issues related to urban areas. Huge and massive datasets can degrade data retrieval and information analysis performance. In addition, the urban environment is very difficult to manage because it involves various types of data, such as multiple types of zoning themes in the case of urban mixed-use development. Thus, a special technique for efficient handling and management of urban data is necessary. This paper proposes a structure called Classified and Clustered Data Constellation (CCDC) for urban data management. CCDC operates on the basis of two filters: classification and clustering. To boost up the performance of information retrieval, CCDC offers a minimal percentage of overlap among nodes and coverage area to avoid repetitive data entry and multipath query. The results of tests conducted on several urban mixed-use development datasets using CCDC verify that it efficiently retrieves their semantic and spatial information. Further, comparisons conducted between CCDC and existing clustering and data constellation techniques, from the aspect of preservation of minimal overlap and coverage, confirm that the proposed structure is capable of preserving the minimum overlap and coverage area among nodes. Our overall results indicate that CCDC is efficient in handling and managing urban data, especially urban mixed-use development applications. Numéro de notice : A2016-531 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.12.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.12.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81614 [article]