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Leveraging Gaussian Processes in Remote Sensing

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  • Emma Foley

    (Bredesen Center, University of Tennessee, Knoxville, TN 37996, USA
    Electrification and Energy Infrastructures Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

Abstract

Power grid reliability is crucial to supporting critical infrastructure, but monitoring and maintenance activities are expensive and sometimes dangerous. Monitoring the power grid involves diverse sources of data, including those inherent to the power operation (inertia, damping, etc.) and ambient atmospheric weather data. TheAutonomous Intelligence Measurements and Sensor Systems (AIMS) project at the Oak Ridge National Laboratory is a project to develop a machine-controlled response team capable of autonomous inspection and reporting with the explicit goal of improved grid reliability. Gaussian processes (GPs) are a well-established Bayesian method for analyzing data. GPs have been successful in satellite sensing for physical parameter estimation, and the use of drones for remote sensing is becoming increasingly common. However, the computational complexity of GPs limits their scalability. This is a challenge when dealing with remote sensing datasets, where acquiring large amounts of data is common. Alternatively, traditional machine learning methods perform quickly and accurately but lack the generalizability innate to GPs. The main objective of this review is to gather burgeoning research that leverages Gaussian processes and machine learning in remote sensing applications to assess the current state of the art. The contributions of these works show that GP methods achieve superior model performance in satellite and drone applications. However, more research using drone technology is necessary. Furthermore, there is not a clear consensus on which methods are the best for reducing computational complexity. This review paves several routes for further research as part of the AIMS project.

Suggested Citation

  • Emma Foley, 2024. "Leveraging Gaussian Processes in Remote Sensing," Energies, MDPI, vol. 17(16), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:3895-:d:1451456
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    References listed on IDEAS

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    1. Matthias Katzfuss & Joseph Guinness & Wenlong Gong & Daniel Zilber, 2020. "Vecchia Approximations of Gaussian-Process Predictions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 383-414, September.
    2. Kou, Peng & Gao, Feng & Guan, Xiaohong, 2013. "Sparse online warped Gaussian process for wind power probabilistic forecasting," Applied Energy, Elsevier, vol. 108(C), pages 410-428.
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