This monograph provides comprehensive coverage of the collection, management, and use of big data obtained from remote sensing. The book begins with an introduction to the basics of big data and remote sensing, laying the groundwork for the more specialized information to follow. The volume then goes on to address a wide variety of topics related to the use and management of remote sensing big data, including hot topics such as analysis through machine learning, cyberinfrastructure, and modeling. Examples on how to use the results of big data analysis of remotely sensed data for concrete decision-making are offered as well. The closing chapters discuss geospatial big data initiatives throughout the world and future challenges and opportunities for remote sensing big data applications.

The audience for this book includes researchers at the intersection of geoscience and data science, senior undergraduate and graduate students, and anyone else interested in how large datasets obtained through remote sensing can be best utilized. The book presents a culmination of 30 years of research from renowned spatial scientists Drs. Liping Di and Eugene Yu.

Discusses the concepts, theory, standards, implementation, and applications of remote sensing big data An advanced guide to remote sensing big data for researchers, students and data scientists in geospatial information Provides the tools and methodologies for applying big data analytics with remote sensing in a variety of contexts

Autorentext

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.

Inhalt
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.