TY - BOOK
T1 - Spatial Big Data Science
T2 - Classification Techniques for Earth Observation Imagery
AU - Jiang, Zhe
AU - Shekhar, Shashi
PY - 2017/7/13
Y1 - 2017/7/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85040981614&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040981614&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-60195-3
DO - 10.1007/978-3-319-60195-3
M3 - Book
AN - SCOPUS:85040981614
SN - 9783319601946
BT - Spatial Big Data Science
PB - Springer International Publishing
ER -