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    Author(s): Forrest M. Hoffman; J. Walter Larson; Richard Tran Mills; Bhorn-Gustaf Brooks; Auroop R. Ganguly; William Hargrove; et al
    Date: 2011
    Source: Procedia Computer Science 4:1450-1455
    Publication Series: Scientific Journal (JRNL)
    Station: Southern Research Station
    PDF: View PDF  (117.54 KB)

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    Description

    From field-scale measurements to global climate simulations and remote sensing, the growing body of very large and long time series Earth science data are increasingly difficult to analyze, visualize, and interpret. Data mining, information theoretic, and machine learning techniques—such as cluster analysis, singular value decomposition, block entropy, Fourier and wavelet analysis, phase-space reconstruction, and artificial neural networks—are being applied to problems of segmentation, feature extraction, change detection, model-data comparison, and model validation. The size and complexity of Earth science data exceed the limits of most analysis tools and the capacities of desktop computers. New scalable analysis and visualization tools, running on parallel cluster computers and supercomputers, are required to analyze data of this magnitude. This workshop will demonstrate how data mining techniques are applied in the Earth sciences and describe innovative computer science methods that support analysis and discovery in the Earth sciences.

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    Citation

    Hoffman, Forrest M.; Larson, J. Walter; Mills, Richard Tran; Brooks, Bjorn-Gustaf J.; Ganguly, Auroop R.; Hargrove, William; et. al. 2011. Data mining, remote sensing, high performance computing, segmentation, change detection, synthesis, visualization. Procedia Computer Science 4:1450-1455.

    Keywords

    Data mining, remote sensing, high performance computing, segmentation, change detection, synthesis, visualization

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