| 000 | 05759nam a2200493 i 4500 | ||
|---|---|---|---|
| 001 | 000697140 | ||
| 003 | OCoLC | ||
| 005 | 20240105153046.0 | ||
| 008 | 170712t20162016caua rb 000 0 eng d | ||
| 020 | _z9781627054621 | ||
| 020 | _z1627054626 | ||
| 020 | _a9781627056700 | ||
| 020 | _a162705670X | ||
| 035 | _a419548 | ||
| 040 |
_aCaBNVSL _bspa _erda _cJ2I _dUIASF |
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| 050 | 4 |
_aQA 402 _bH47.2016 |
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| 245 | 0 | 0 |
_aHeterogeneous spatial data : _bfusion, modeling, and analysis for GIS applications / _cGiuseppe Patanè and Michela Spagnuolo, editors, CNR-IMATI. |
| 264 | 1 |
_aSan Rafael, California : _bMorgan & Claypool Publishers, _c2016 |
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| 264 | 4 | _c©2016 | |
| 300 |
_axxv, 129 páginas : _bilustraciones ; _c24 cm |
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| 336 |
_atexto _btxt _2rdacontent |
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| 337 |
_asin mediación _bn _2rdamedia |
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| 338 |
_avolumen _bnc _2rdacarrier |
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| 490 | 1 |
_aSynthesis lectures on visual computing _v24 |
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| 504 | _aIncluye bibliografía (páginas 101-128). | ||
| 505 | 0 | _a1. Spatio-temporal data fusion / Roderik Lindenbergh, Roberto Giachetta, Giuseppe Patanè -- 1.1 Geospatial data: acquisition and properties -- 1.2 Spatio-temporal data fusion -- 1.3 Data alignment: registration methods -- 1.3.1 Direct georeferencing -- 1.3.2 Target and feature-based registration -- 1.3.3 Low-level feature matching -- 1.3.4 Examples: -- 1.4 Harmonize support: interpolation methods -- 1.4.1 Deterministic methods -- 1.4.2 Stochastic methods -- 1.5 Satellite time series analysis -- 1.5.1 Improving spatial and temporal resolution -- 1.5.2 Estimating missing data -- 1.5.3 Vegetation monitoring -- 1.6 Spatio-temporal data access methods -- 1.7 Discussion: sensors, software, and practical issues. | |
| 505 | 8 | _a2. Spatial and environmental data approximation / Vibeke Skytt, Giuseppe Patanè, Oliver Barrowclough, Tor Dokken, Michela Spagnuolo -- 2.1 Data approximation -- 2.2 Spline representations and approximations -- 2.2.1 Parameterization -- 2.2.2 Tensor product splines -- 2.2.3 Locally refined splines -- 2.2.4 Spline approximations -- 2.2.5 Adapting to boundaries and features -- 2.3 Meshless approximations -- 2.3.1 Moving least-squares surfaces -- 2.3.2 Implicit approximation with radial basis functions -- 2.3.3 Kriging -- 2.3.4 Computational cost. | |
| 505 | 8 | _a3. Feature extraction / Silvia Biasotti, Andrea Cerri, Giuseppe Patanè, Michela Spagnuolo -- 3.1 3D data analysis -- 3.1.1 Curvature evaluation -- 3.1.2 Primitive and curvature-based segmentation -- 3.1.3 3D feature descriptors -- 3.2 3D surfaces studied by means of scalar fields -- 3.2.1 Critical point-oriented characterization -- 3.2.2 Topological persistence -- 3.2.3 Contour-based characterization -- 3.2.4 Morse and Morse-Smale complexes and surface networks -- 3.2.5 Contour trees and Reeb graphs. | |
| 505 | 8 | _a4. Applications to surface approximation and rainfall analysis / Giuseppe Patanè, Andrea Cerri, Vibeke Skytt, Simone Pittaluga, Silvia Biasotti, Davide Sobrero, Tor Dokken, Michela Spagnuolo -- 4.1 Surface approximation with LR B-splines -- 4.2 Approximation and analysis of rainfall data -- 4.3 Analysis of topological changes in GIS data. | |
| 505 | 8 | _a5. Conclusions -- Bibliography -- Authors' biographies. | |
| 520 | 3 | _aNew data acquisition techniques are emerging and are providing fast and efficient means for multidimensional spatial data collection. Airborne LIDAR surveys, SAR satellites, stereophotogrammetry 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 |
_aSpatial data infrastructures _xMathematical models. |
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| 650 | 4 |
_aInfraestructuras de datos espaciales _xModelos matemáticos |
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| 650 | 0 |
_aGeographic information systems _xMathematical models. |
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| 650 | 4 |
_aSistemas de información geográfica _xModelos matemáticos |
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| 700 | 1 |
_aPatanè, Giuseppe _d1974- _eeditor |
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| 700 | 1 |
_aSpagnuolo, Michela _eeditor |
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| 830 | 0 |
_aSynthesis lectures on visual computing _v# 24. |
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| 942 | 1 | _cNEWBFXC1 | |
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_c652809 _d652809 |
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_851 _gRonald RUIZ |
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