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Special Features of Remote Sensing Big Data

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Remote Sensing Big Data

Part of the book series: Springer Remote Sensing/Photogrammetry ((SPRINGERREMO))

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Abstract

This chapter briefly covers the five core dimensions of remote sensing big data, that is, volume, variety, velocity, veracity, and value. There are also 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 relationships among elements (Shelestov et al. Front Earth Sci 5 2017), and Viscosity for the complexity (Manogaran and Lopez Int J Biomed Eng Technol 25:182, 2017). Remote sensing big data may cover as many Vs as other big data (Khan et al. Proceedings of the International Conference on Omni-Layer Intelligent Systems - COINS ‘19. ACM Press, Crete, Greece, 2019).

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Authors and Affiliations

  1. Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USA

    Li** Di & Eugene Yu

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  1. Li** Di
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  2. Eugene Yu
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Di, L., Yu, E. (2023). Special Features of Remote Sensing Big Data. In: Remote Sensing Big Data. Springer Remote Sensing/Photogrammetry. Springer, Cham. https://doi.org/10.1007/978-3-031-33932-5_3

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