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008 220601s2016 sz | s |||| 0|eng d
020 _a9783031025891
_9978-3-031-02589-1
024 7 _a10.1007/978-3-031-02589-1
_2doi
050 4 _aQA1-939
072 7 _aPB
_2bicssc
072 7 _aMAT000000
_2bisacsh
072 7 _aPB
_2thema
082 0 4 _a510
_223
100 1 _aPatanè, Giuseppe.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979072
245 1 0 _aHeterogeneous Spatial Data
_h[electronic resource] :
_bFusion, Modeling, and Analysis for GIS Applications /
_cby Giuseppe Patanè, Michela Spagnuolo.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXXV, 129 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Visual Computing: Computer Graphics, Animation, Computational Photography and Imaging,
_x2469-4223
505 0 _aList of Figures -- List of Tables -- Preface -- Acknowledgments -- Spatio-temporal Data Fusion -- Spatial and Environmental Data Approximation -- Feature Extraction -- Applications to Surface Approximation and Rainfall Analysis -- Conclusions -- Bibliography -- Authors' Biographies.
520 _aNew data acquisition techniques are emerging and are providing fast and efficient means for multidimensional spatial data collection. Airborne LIDAR surveys, SAR satellites, stereo-photogrammetry and mobile mapping systems are increasingly used for the digital reconstruction of the environment. All these systems provide extremely high volumes of raw data, often enriched with other sensor data (e.g., beam intensity). Improving methods to process and visually analyze this massive amount of geospatial and user-generated data is crucial to increase the efficiency of organizations and to better manage societal challenges. Within this context, this book proposes an up-to-date view of computational methods and tools for spatio-temporal data fusion, multivariate surface generation, and feature extraction, along with their main applications for surface approximation and rainfall analysis. The book is intended to attract interest from different fields, such as computer vision, computer graphics,geomatics, and remote sensing, working on the common goal of processing 3D data. To this end, it presents and compares methods that process and analyze the massive amount of geospatial data in order to support better management of societal challenges through more timely and better decision making, independent of a specific data modeling paradigm (e.g., 2D vector data, regular grids or 3D point clouds). We also show how current research is developing from the traditional layered approach, adopted by most GIS softwares, to intelligent methods for integrating existing data sets that might contain important information on a geographical area and environmental phenomenon. These services combine traditional map-oriented visualization with fully 3D visual decision support methods and exploit semantics-oriented information (e.g., a-priori knowledge, annotations, segmentations) when processing, merging, and integrating big pre-existing data sets.
650 0 _aMathematics.
_911584
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_979073
650 1 4 _aMathematics.
_911584
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
700 1 _aSpagnuolo, Michela.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979074
710 2 _aSpringerLink (Online service)
_979075
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031014611
776 0 8 _iPrinted edition:
_z9783031037177
830 0 _aSynthesis Lectures on Visual Computing: Computer Graphics, Animation, Computational Photography and Imaging,
_x2469-4223
_979076
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02589-1
912 _aZDB-2-SXSC
942 _cEBK
999 _c84710
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