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Spatial Big Data Science: Classification Techniques for Earth Observation Imagery

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Personen und Körperschaften: Jiang, Zhe (VerfasserIn), Shekhar, Shashi (Sonstige)
Titel: Spatial Big Data Science: Classification Techniques for Earth Observation Imagery/ by Zhe Jiang, Shashi Shekhar
Format: E-Book
Sprache: Englisch
veröffentlicht:
Cham Springer 2017
Gesamtaufnahme: SpringerLink
Springer eBook Collection
Schlagwörter:
Computer science
Physical geography
Data mining
Remote sensing
Computer Science
Quelle: Verbunddaten SWB
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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 
520 |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 
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contents 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, 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
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spelling Jiang, Zhe (DE-588)1097590429 (DE-627)857299379 (DE-576)468871373 aut, Spatial Big Data Science Classification Techniques for Earth Observation Imagery by Zhe Jiang, Shashi Shekhar, Cham Springer 2017, Online-Ressource (XV, 131 p. 37 illus., 27 illus. in color, online resource), Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, SpringerLink Bücher, Springer eBook Collection Computer Science, 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, 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, Computer science, Physical geography, Data mining, Remote sensing, Computer Science, Shekhar, Shashi (DE-627)1255829095 (DE-576)185829090 oth, 9783319601946, Druckausg. 978-3-319-60194-6, Printed edition 9783319601946, https://doi.org/10.1007/978-3-319-60195-3 B:SPRINGER Verlag lizenzpflichtig Volltext, (DE-627)894892274, http://dx.doi.org/10.1007/978-3-319-60195-3 DE-Ch1, DE-Ch1 epn:3371372200 2017-08-07T10:48:50Z, http://dx.doi.org/10.1007/978-3-319-60195-3 DE-Zwi2, DE-Zwi2 epn:3371372367 2017-08-07T10:48:50Z
spellingShingle Jiang, Zhe, Spatial Big Data Science: Classification Techniques for Earth Observation Imagery, 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, 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, Computer science, Physical geography, Data mining, Remote sensing, Computer Science
swb_id_str 491759363
title Spatial Big Data Science: Classification Techniques for Earth Observation Imagery
title_auth Spatial Big Data Science Classification Techniques for Earth Observation Imagery
title_full Spatial Big Data Science Classification Techniques for Earth Observation Imagery by Zhe Jiang, Shashi Shekhar
title_fullStr Spatial Big Data Science Classification Techniques for Earth Observation Imagery by Zhe Jiang, Shashi Shekhar
title_full_unstemmed Spatial Big Data Science Classification Techniques for Earth Observation Imagery by Zhe Jiang, Shashi Shekhar
title_short Spatial Big Data Science
title_sort spatial big data science classification techniques for earth observation imagery
title_sub Classification Techniques for Earth Observation Imagery
title_unstemmed Spatial Big Data Science: Classification Techniques for Earth Observation Imagery
topic Computer science, Physical geography, Data mining, Remote sensing, Computer Science
topic_facet Computer science, Physical geography, Data mining, Remote sensing, Computer Science
url https://doi.org/10.1007/978-3-319-60195-3, http://dx.doi.org/10.1007/978-3-319-60195-3