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007			cr nn 008mamaa
008			220601s2016 sz | s |||| 0|eng d
020			_a9783031025891
024	7		_a10.1007/978-3-031-02589-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA402 _b2016 EB
100	1		_aPatanè, Giuseppe, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686409 _d1974-
245	1	0	_aHeterogeneous Spatial Data : _bFusion, Modeling, and Analysis for GIS Applications _cby Giuseppe Patanè, Michela Spagnuolo
250			_a1st edition 2016
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XXV, 129 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2016
650		7	_2embne _9263853 _aSistemas de información geográfica _xModelos matemáticos
700	1		_aSpagnuolo, Michela _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _993639
776	0	8	_iPrinted edition: _z9783031014611
776	0	8	_iPrinted edition: _z9783031037177
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02589-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _esc _zSI