Leveraging Gaussian Processes in Remote Sensing

Emma Foley

doi:10.20944/preprints202406.0526.v1

Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 6 June 2024 / Approved: 7 June 2024 / Online: 10 June 2024 (08:41:02 CEST)

Power grid reliability is crucial to supporting critical infrastructure, but monitoring and maintenance activities are expensive and sometimes dangerous. Remote sensing enables real time data monitoring and collection related to environmental and industrial processes, like the power grid system. Monitoring the power grid successfully involves diverse sources of data including that inherent to the power operation and ambient atmospheric weather data. Detection methods can identify anomalies like arcing, and climate data in particular is becoming increasingly important in order to maintain power supply. Gaussian processes (GPs) are a well-established Bayesian method for analyzing data. However, the computational complexity of GPs limits their scalability. This is a challenge when dealing with remote sensing datasets, where acquiring a significant amount of data is common. Alternatively, traditional machine learning methods perform quickly and accurately, but lack the generalizability innate to GPs. The focus of this review is burgeoning research that leverages Gaussian processes and machine learning in remote sensing applications.

remote sensing; grid management; literature review

Engineering, Electrical and Electronic Engineering

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Leveraging Gaussian Processes in Remote Sensing

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