Spatial Big Data Science : Classification Techniques for Earth Observation Imagery

Zhe Jiang

ISBN 10: 3319868020 / ISBN 13: 9783319868028

Editore: Springer, Berlin, Springer International Publishing, Springer, 2018

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - 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.

Codice articolo 9783319868028

Dati bibliografici

• Titolo: Spatial Big Data Science : Classification Techniques for Earth Observation Imagery
• Editore: Springer, Berlin, Springer International Publishing, Springer
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 3319868020
• ISBN 13: 9783319868028
• Rilegatura: Taschenbuch
• Condizione: Neu

Descrizione articolo

Riassunto

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.