Spatial Big Data Science Classification Techniques for Earth Observa
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 analyzin
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atial Big Data Science Classification Techniques for Earth Observation Imagery
Spatial Big Data Science
Zhe Jiang Shashi Shekhar •
Spatial Big Data Science Classification Techniques for Earth Observation Imagery
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Shashi Shekhar Department of Computer Science University of Minnesota Minneapolis, MN USA
Zhe Jiang Department of Computer Science University of Alabama Tuscaloosa, AL USA
ISBN 978-3-319-60194-6 DOI 10.1007/978-3-319-60195-3
ISBN 978-3-319-60195-3
(eBook)
Library of Congress Control Number: 2017943225 © Springer International Publishing AG 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
To those who have generously helped me during my Ph.D. study. —Zhe Jiang
Preface
With the advancement of remote sensing technology, wide usage of GPS devices in vehicles and cell phones, popularity of mobile applications, crowd sourcing, and geographic information systems, as well as cheaper data storage devices, enormous geo-referenced data is being collected from broader disciplines ranging from business to science and engineering. The volume, velocity, and variety of such geo-reference data are exceeding the capability of traditional spatial computing platform (also called Spatial big data or SBD). Emerging spatial big data 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. This book discusses the current techniques for s
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