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Spatial big data science = classification techniques for Earth observation imagery /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Spatial big data science/ by Zhe Jiang, Shashi Shekhar.
Reminder of title:	classification techniques for Earth observation imagery /
Author:	Jiang, Zhe.
other author:	Shekhar, Shashi.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xv, 131 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Geographic information systems. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-60195-3
ISBN:	9783319601953

Spatial big data science = classification techniques for Earth observation imagery /
Jiang, Zhe.

Spatial big data scienceclassification techniques for Earth observation imagery /[electronic resource] :by Zhe Jiang, Shashi Shekhar. - Cham :Springer International Publishing :2017. - xv, 131 p. :ill., digital ;24 cm.

Part I Overview of Spatial Big Data Analytics -- 1 Spatial Big -- 2 Spatial and Spatiotemporal Big Data science -- Part II Classification of Earth Observation Imagery Big Data -- 3 Overview of Earth Imagery Classification -- 4 Spatial Information Gain Based Spatial Decision Tree -- 5 Focal-Test-Based Spatial Decision Tree -- 6 Spatial Ensemble Learning -- Part III Future Research Needs -- 7 Future Research Needs -- References.

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.

ISBN: 9783319601953

Standard No.: 10.1007/978-3-319-60195-3doiSubjects--Topical Terms:

554796
Geographic information systems.

LC Class. No.: G70.212

Dewey Class. No.: 910.285

Spatial big data science = classification techniques for Earth observation imagery /
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505 0 $a Part I Overview of Spatial Big Data Analytics -- 1 Spatial Big -- 2 Spatial and Spatiotemporal Big Data science -- Part II Classification of Earth Observation Imagery Big Data -- 3 Overview of Earth Imagery Classification -- 4 Spatial Information Gain Based Spatial Decision Tree -- 5 Focal-Test-Based Spatial Decision Tree -- 6 Spatial Ensemble Learning -- Part III Future Research Needs -- 7 Future Research Needs -- References.
520 $a 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.
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