Titlebook: Remote Sensing Big Data; Liping Di,Eugene Yu Book 2023 Springer Nature Switzerland AG 2023 Geospatial big data.Remote sensing.Cyberinfrast

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Remote Sensing Big Data Collection Challenges and Cyberinfrastructure and Sensor Web Solutions,sensor web cyberinfrastructure, Self-adaptive Earth Predictive System (SEPS), is briefed. Example applications of SEPS as a cyberinfrastructure building framework were discussed, including areas of climate, weather, disasters, and agriculture.

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Special Features of Remote Sensing Big Data,so other Vs to be explored, like Visualization for effectively high-dimensional visuals and exploration (Huang et al. J Integrat Agric 17:1915–1931, 2018), Volatility for data time-sensitivity (Antunes et al. GIScience Remote Sens 56:536–553, 2019), Validity for the exploration of hidden relationshi

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Remote Sensing Big Data Computing,riefly reviewed, that is, stand-alone, centralized, and distributed. The high-performance computing environment is also briefly reviewed, that is, supercomputer, grid computing, and cloud computing. Service-oriented architecture is discussed in detail. Specialized big data processing frameworks are

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Remote Sensing Big Data Management,tion and granule levels) and access. This chapter first discusses different aspects of remote sensing big data governance, which blueprint standards and policy. Secondly, remote sensing data curation covers metadata, archiving, cataloging, quality assessment, and provenance/lineage. Finally, dissemi

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Big Data Analytics for Remote Sensing: Concepts and Standards,r remote sensing big data analytics are discussed. Two standard efforts on big data analytics are briefly covered, that is, ISO (International Organization for Standardization) Big Data Working Group and IEEE (Institute of Electrical and Electronics Engineers) Big Data Standard group.

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Big Data Analytic Platforms, a variety of infrastructures for big analytics platforms, including a peer-to-peer system, clusters, high-performance computers, grid computing, and cloud computing. There are several options for storage and memory management, including file-based, distributed file systems, SQL (Structured Query La