Spatial Big Data Science - Classification Techniques for Earth Observation Imagery (Hardcover, 1st ed. 2017)

Emerging Spatial Big Data (SBD) has transformative potential in solving many grand societal challenges such as water resource management, food security, disaster response, and transportation. However, significant computational challenges exist in analyzing SBD due to the unique spatial characteristics including spatial autocorrelation, anisotropy, heterogeneity, multiple scales and resolutions which is illustrated in this book. This book also discusses current techniques for, spatial big data science with a particular focus on classification techniques for earth observation imagery big data. Specifically, the authors introduce several recent spatial classification techniques, such as spatial decision trees and spatial ensemble learning. Several potential future research directions are also discussed. This book targets an interdisciplinary audience including computer scientists, practitioners and researchers working in the field of data mining, big data, as well as domain scientists working in earth science (e.g., hydrology, disaster), public safety and public health. Advanced level students in computer science will also find this book useful as a reference.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Release date:	July 2017
First published:	2017
Authors:	Zhe Jiang • Shashi Shekhar
Dimensions:	235 x 155 x 15mm (L x W x T)
Format:	Hardcover
Pages:	131
Edition:	1st ed. 2017
ISBN-13:	978-3-319-60194-6
Categories:	Books > Earth & environment > Earth sciences > General Books > Earth & environment > Geography > Cartography, geodesy & geographic information systems (GIS) > Geographical information systems (GIS) Books > Earth & environment > Geography > Cartography, geodesy & geographic information systems (GIS) > Remote sensing Books > Computing & IT > Applications of computing > Databases > Data mining Promotions
LSN:	3-319-60194-6
Barcode:	9783319601946

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