Remote Sensing Big Data

Dr. Liping Di serves as Professor and Director of the Center for Spatial Information Science and Systems at George Mason University in Virginia, USA. He is internationally known for his extraordinary contributions to the geospatial information science/geoinformatics, especially to the development of geospatial interoperability technology and the federal, national, and international geographic information and remote sensing standards. He was one of the core members for the development of the NASA EOSDIS data standards, and is a pioneer in the development of web-based, advanced, distributed geospatial systems and tools. Dr. Di has engaged in the geoinformatics and Earth system research for more than twenty-five years and has published over 500 publications. Dr. Eugene Yu is a Research Professor and the Associate Director of the Center for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia, USA. Dr. Yu received the B.Sc. degree in physical geography from the Peking University, Beijing, China, the M.Sc. in environmental remote sensing from the University of Aberdeen, Aberdeen, U.K., the M.S. in Information Systems and the M.S. in computer science from George Mason University, Fairfax, Virginia, USA, and the Ph.D. in geography with the focus on remote sensing and geographic information systems from the Indiana State University, Terre Haute, Indiana, USA. His research interests include geographic information systems, remote sensing, intelligent image understanding, Sensor Web, semantic Web, computational vision, agro-informatics, and robotics.

• Chapter 1: Introduction.- Chapter 2: Remote Sensing.- Chapter 3: Special Features of Remote Sensing Big Data.- Chapter 4: Remote Sensing Big Data Collection Challenges and Cyberinfrastructure and Sensor Web Solutions.- Chapter 5: Remote Sensing Big Data Computing.- Chapter 6: Remote Sensing Big Data Management.- Chapter 7: Standards for Big Data Management.- Chapter 8: Implementation Examples of Big Data Management Systems for Remote Sensing.- Chapter 9: Big Data Analytics for Remote Sensing-Concepts and standards.- Chapter 10: Big Data Analytic Platforms.- Chapter 11: Algorithmic Design Considerations of Big Data Analytics.- Chapter 12: Machine learning and data mining algorithms for geospatial big data.- Chapter 13: Modeling, prediction, and decision making based on remote sensing big data.- Chapter 14: Examples of remote sensing applications of big data analytics-fusion of diverse earth observation data.- Chapter 15: Examples of remote sensing applications of big data analytics-agricultural drought monitoring and forecasting.- Chapter 16: Examples of remote sensing applications of big data analytics-land cover time series creation.- Chapter 17: Geospatial big data initiatives in the world.- Chapter 18: Challenges and opportunities in remote sensing big data.