LEADER 04064nam 2200577 450 001 9910483678703321 005 20231006191936.0 010 $a3-030-55462-7 024 7 $a10.1007/978-3-030-55462-0 035 $a(CKB)4100000011918791 035 $a(DE-He213)978-3-030-55462-0 035 $a(MiAaPQ)EBC6607534 035 $a(Au-PeEL)EBL6607534 035 $a(OCoLC)1250347956 035 $a(PPN)255885954 035 $a(EXLCZ)994100000011918791 100 $a20220119d2021 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aHandbook of big geospatial data /$fMartin Werner, Yao-Yi Chiang, editors 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$d©2021 215 $a1 online resource (XI, 641 p. 222 illus., 148 illus. in color.) 311 $a3-030-55461-9 327 $aI Introduction -- II Spatial Big Data Platforms & Infrastructures -- III Spatial Data Acquisition -- IV Indexing and Retrieval of Spatial Big Data -- V Scalable Algorithms for Spatial Analytics -- VI Data Mining, Machine Learning and Artificial Intelligence -- VII Visualization & Interaction -- VIII Applications. 330 $aThis handbook covers a wide range of topics related to the collection, processing, analysis, and use of geospatial data in their various forms. This handbook provides an overview of how spatial computing technologies for big data can be organized and implemented to solve real-world problems. Diverse subdomains ranging from indoor mapping and navigation over trajectory computing to earth observation from space, are also present in this handbook. It combines fundamental contributions focusing on spatio-textual analysis, uncertain databases, and spatial statistics with application examples such as road network detection or colocation detection using GPUs. In summary, this handbook gives an essential introduction and overview of the rich field of spatial information science and big geospatial data. It introduces three different perspectives, which together define the field of big geospatial data: a societal, governmental, and governance perspective. It discusses questions of how the acquisition, distribution and exploitation of big geospatial data must be organized both on the scale of companies and countries. A second perspective is a theory-oriented set of contributions on arbitrary spatial data with contributions introducing into the exciting field of spatial statistics or into uncertain databases. A third perspective is taking a very practical perspective to big geospatial data, ranging from chapters that describe how big geospatial data infrastructures can be implemented and how specific applications can be implemented on top of big geospatial data. This would include for example, research in historic map data, road network extraction, damage estimation from remote sensing imagery, or the analysis of spatio-textual collections and social media. This multi-disciplinary approach makes the book unique. This handbook can be used as a reference for undergraduate students, graduate students and researchers focused on big geospatial data. Professionals can use this book, as well as practitioners facing big collections of geospatial data. 606 $aGeospatial data$xComputer processing$vHandbooks, manuals, etc 606 $aBig data$vHandbooks, manuals, etc 606 $aBig data 606 $aDades geoespacials$2thub 606 $aDades massives$2thub 608 $aLlibres electrònics$2thub 615 0$aGeospatial data$xComputer processing 615 0$aBig data 615 0$aBig data. 615 7$aDades geoespacials 615 7$aDades massives 676 $a005.7 702 $aWerner$b Martin 702 $aChiang$b Yao-Yi 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483678703321 996 $aHandbook of Big Geospatial Data$91965578 997 $aUNINA