A Bootstrapping Convolutional Neural Network Technique for Optimizing Automated Detection of Equatorial Plasma Bubbles by Optical All‐Sky Imagers

A Bootstrapping Convolutional Neural Network Technique for Optimizing Automated Detection of Equatorial Plasma Bubbles by Optical All‐Sky Imagers

Abstract Equatorial plasma bubbles (EPBs) disrupt satellite‐based communication and navigation systems, particularly in equatorial regions. Reliable detection and classification of EPBs from all‐sky imager (ASI) images are essential for accurate space weather monitoring and forecasting. This study p...

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Main Authors: Daniel Okoh, Claudio Cesaroni, Babatunde Rabiu, Kazuo Shiokawa, Yuichi Otsuka, Samuel Ogunjo, Aderonke Akerele, John Bosco Habarulema, Bruno Nava, Yenca Migoya‐Orué, Punyawi Jamjareegulgarn, Adeniran Seun, Ogechi Adama, George Ochieng, James Ameh, Adero Awuor, Paul Baki
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2025-06-01
Series:Earth and Space Science
Subjects:
all‐sky imager
convolutional neural networks
equatorial plasma bubbles
airglow images
bootstrapping
machine learning
Online Access:https://doi.org/10.1029/2024EA004117
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https://doi.org/10.1029/2024EA004117

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