Manual of Digital Earth.

• Author: Guo, Huadong.
• TeilnehmendeR: Goodchild, Michael F.; Annoni, Alessandro.
• Place / Publishing House: Singapore : : Springer Singapore Pte. Limited,, 2019.; ©2020.
• Year of Publication: 2019
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (846 pages)

Table of Contents

• Intro
• Preface
• Acknowledgements
• List of Editors
• Editors-in-Chief
• Managing Editors
• Contents
• About the Editors-in-Chief
• Digital Earth Technologies
• 2 Digital Earth Platforms
• 2.1 Introduction
• 2.2 Discrete Global Grid Systems
• 2.2.1 Initial Domain
• 2.2.2 Cell Type
• 2.2.3 Refinement
• 2.2.4 Projection
• 2.2.5 Indexing
• 2.3 Scientific Digital Earths
• 2.4 Public and Commercial Digital Earth Platforms
• 2.4.1 Latitude/Longitude Grids
• 2.4.2 Geodesic DGGSs
• 2.4.3 Installations: DESP
• 2.5 Discrete Global Grid System Standards
• 2.5.1 Standardization of Discrete Global Grid Systems
• 2.5.2 Core Requirements of the OGC DGGS Abstract Specification
• 2.5.3 The Future of the DGGS Standard
• 2.5.4 Linkages Between DGGS and Other Standards Activities
• References
• 3 Remote Sensing Satellites for Digital Earth
• 3.1 Development of Remote Sensing
• 3.1.1 Overview of Remote Sensing
• 3.1.2 Development of Remote Sensing Satellites
• 3.2 Land Observation Satellites
• 3.2.1 US Land Observation Satellites
• 3.2.2 European Land Observation Satellites
• 3.2.3 China's Land Observation Satellites
• 3.2.4 Other Land Observation Satellites
• 3.3 Ocean Observation Satellites
• 3.3.1 US Ocean Observation Satellites
• 3.3.2 European Ocean Observation Satellites
• 3.3.3 China's Ocean Observation Satellites
• 3.3.4 Other Ocean Observation Satellites
• 3.4 Meteorological Observation Satellites
• 3.4.1 US Meteorological Observation Satellites
• 3.4.2 European Meteorological Observation Satellites
• 3.4.3 China's Meteorological Observation Satellites
• 3.4.4 Other Meteorological Observation Satellites
• 3.5 Trends in Remote Sensing for Digital Earth
• References
• 4 Satellite Navigation for Digital Earth
• 4.1 Introduction
• 4.2 Global Navigation Satellite System
• 4.2.1 BDS
• 4.2.2 GPS
• 4.2.3 GLONASS.
• 4.2.4 Galileo
• 4.3 GNSS Augmentation Systems
• 4.3.1 Wide-Area Differential Augmentation System
• 4.3.2 Global Differential Precise Positioning System
• 4.3.3 Local Area Differential Augmentation System
• 4.3.4 Local Area Precise Positioning System
• 4.4 Applications in Digital Earth Case Studies
• 4.4.1 Terrestrial Reference System
• 4.4.2 Time System
• 4.4.3 High-Precision Positioning
• 4.4.4 Location-Based Service
• References
• 5 Geospatial Information Infrastructures
• 5.1 Introduction
• 5.2 A Brief History of Geospatial Information Infrastructures
• 5.2.1 Geospatial Information Infrastructure Milestones
• 5.2.2 Architectural Evolutions in Geospatial Information Infrastructure Development
• 5.3 Geospatial Information Infrastructures Today
• 5.3.1 The Evolution of Geospatial Information on the Web
• 5.3.2 Geospatial Information Infrastructures Champion Openness
• 5.3.3 Capacity Building and Learning for Geospatial Information Infrastructures
• 5.4 Recent Challenges and Potential for Improvement
• 5.4.1 Strengthened Role of Semantics
• 5.4.2 Is Spatial Still Special?
• 5.5 Conclusion and Outlook
• References
• 6 Geospatial Information Processing Technologies
• 6.1 Introduction
• 6.2 High-Performance Computing
• 6.2.1 The Concept of High-Performance Computing: What and Why
• 6.2.2 High-Performance Computing Platforms
• 6.2.3 Spatial Database Management Systems and Spatial Data Mining
• 6.2.4 Applications Supporting Digital Earth
• 6.2.5 Research Challenges and Future Directions
• 6.3 Online Geospatial Information Processing
• 6.3.1 Web Service-Based Online Geoprocessing
• 6.3.2 Web (Coverage) Processing Services
• 6.3.3 Online Geoprocessing Applications in the Context of Digital Earth
• 6.3.4 Research Challenges and Future Directions
• 6.4 Distributed Geospatial Information Processing.
• 6.4.1 The Concept of Distributed Geospatial Information Processing: What and Why
• 6.4.2 Fundamental Concepts and Techniques
• 6.4.3 Application Supporting Digital Earth
• 6.4.4 Research Challenges and Future Directions
• 6.5 Discussion and Conclusion
• References
• 7 Geospatial Information Visualization and Extended Reality Displays
• 7.1 Introduction
• 7.2 Visualizing Geospatial Information: An Overview
• 7.2.1 Representation
• 7.2.2 User Interaction and Interfaces
• 7.3 Understanding Users: Cognition, Perception, and User-Centered Design Approaches for Visualization
• 7.3.1 Making Visualizations Work for Digital Earth Users
• 7.4 Geovisual Analytics
• 7.4.1 Progress in Geovisual Analytics
• 7.4.2 Big Data, Digital Earth, and Geovisual Analytics
• 7.5 Visualizing Movement
• 7.5.1 Trajectory Maps: The Individual Journey
• 7.5.2 Flow Maps: Aggregated Flows Between Places
• 7.5.3 Origin-Destination (OD) Maps
• 7.5.4 In-Flow, Out-Flow and Density of Moving Objects
• 7.6 Immersive Technologies-From Augmented to Virtual Reality
• 7.6.1 Essential Concepts for Immersive Technologies
• 7.6.2 Augmented Reality
• 7.6.3 Mixed Reality
• 7.7 Virtual Reality
• 7.7.1 Virtual Geographic Environments
• 7.7.2 Foundational Structures of VGEs
• 7.8 Dashboards
• 7.9 Conclusions
• References
• 8 Transformation in Scale for Continuous Zooming
• 8.1 Continuous Zooming and Transformation in Scale: An Introduction
• 8.1.1 Continuous Zooming: Foundation of the Digital Earth
• 8.1.2 Transformation in Scale: Foundation of Continuous Zooming
• 8.1.3 Transformation in Scale: A Fundamental Issue in Disciplines Related to Digital Earth
• 8.2 Theories of Transformation in Scale
• 8.2.1 Transformation in Scale: Multiscale Versus Variable Scale
• 8.2.2 Transformations in Scale: Euclidean Versus Geographical Space.
• 8.2.3 Theoretical Foundation for Transformation in Scale: The Natural Principle
• 8.3 Models for Transformations in Scale
• 8.3.1 Data Models for Feature Representation: Space-Primary Versus Feature-Primary
• 8.3.2 Space-Primary Hierarchical Models for Transformation in Scale
• 8.3.3 Feature-Primary Hierarchical Models for Transformation in Scale
• 8.3.4 Models of Transformation in Scale for Irregular Triangulation Networks
• 8.3.5 Models for Geometric Transformation of Map Data in Scale
• 8.3.6 Models for Transformation in Scale of 3D City Representations
• 8.4 Mathematical Solutions for Transformations in Scale
• 8.4.1 Mathematical Solutions for Upscaling Raster Data: Numerical and Categorical
• 8.4.2 Mathematical Solutions for Downscaling Raster Data
• 8.4.3 Mathematical Solutions for Transformation (in Scale) of Point Set Data
• 8.4.4 Mathematical Solution for Transformation (in Scale) of Individual Lines
• 8.4.5 Mathematical Solutions for Transformation (in Scale) of Line Networks
• 8.4.6 Mathematical Solutions for Transformation of a Class of Area Features
• 8.4.7 Mathematical Solutions for Transformation (in Scale) of Spherical and 3D Features
• 8.5 Transformation in Scale: Final Remarks
• References
• 9 Big Data and Cloud Computing
• 9.1 Introduction
• 9.2 Big Data Sources
• 9.3 Big Data Analysis Methods
• 9.3.1 Data Preprocessing
• 9.3.2 Statistical Analysis
• 9.3.3 Nonstatistical Analysis
• 9.4 Architecture for Big Data Analysis
• 9.4.1 Data Storage Layer
• 9.4.2 Data Query Layer
• 9.4.3 Data Processing Layer
• 9.5 Cloud Computing for Big Data
• 9.5.1 Cloud Computing and Other Related Computing Paradigms
• 9.5.2 Introduction to Cloud Computing
• 9.5.3 Cloud Computing to Support Big Data
• 9.6 Case Study: EarthCube/DataCube
• 9.6.1 EarthCube
• 9.6.2 Data Cube
• 9.7 Conclusion
• References.
• 10 Artificial Intelligence
• 10.1 Introduction
• 10.2 Traditional and Statistical Machine Learning
• 10.2.1 Supervised Learning
• 10.2.2 Unsupervised Learning
• 10.2.3 Dimension Reduction
• 10.3 Deep Learning
• 10.3.1 Convolutional Networks
• 10.3.2 Recurrent Neural Networks
• 10.3.3 Variational Autoencoder
• 10.3.4 Generative Adversarial Networks (GANs)
• 10.3.5 Dictionary-Based Approaches
• 10.3.6 Reinforcement Learning
• 10.4 Discussion
• 10.4.1 Reproducibility
• 10.4.2 Ownership and Fairness
• 10.4.3 Accountability
• 10.5 Conclusion
• References
• 11 Internet of Things
• 11.1 Introduction
• 11.2 Definitions and status quo of the IoT
• 11.2.1 One Concept, Many Definitions
• 11.2.2 Our Definition
• 11.2.3 Early Works on the Interplay Between DE and the IoT
• 11.2.4 IoT Standards Initiatives from DE
• 11.3 Interplay Between the IoT and DE
• 11.3.1 Discoverability, Acquisition and Communication of Spatial Information
• 11.3.2 Spatial Understanding of Objects and Their Relationships
• 11.3.3 Taking Informed Actions and Acting Over the Environment (ACT)
• 11.4 Case Studies on Smart Scenarios
• 11.5 Frictions and Synergies Between the IoT and DE
• 11.5.1 Discoverability, Acquisition and Communication of Spatial Information
• 11.5.2 Spatial Understanding of Objects and Their Relationships
• 11.5.3 Taking Informed Actions and Acting Over the Environment
• 11.6 Conclusion and Outlook for the Future of the IoT in Support of DE
• References
• 12 Social Media and Social Awareness
• 12.1 Introduction: Electronic Footprints on Digital Earth
• 12.2 Multifaceted Implications of Social Media
• 12.3 Opportunities: Human Dynamics Prediction
• 12.3.1 Public Health
• 12.3.2 Emergency Response
• 12.3.3 Decision Making
• 12.3.4 Social Equity Promotion
• 12.4 Challenges: Fake Electronic Footprints
• 12.4.1 Rumors.
• 12.4.2 Location Spoofing.