Mathematics and System Engineering Faculty Publications

Document Type

Article

Publication Title

IEEE Access

Abstract

General aviation pilots who encounter hazardous weather face a heightened risk of fatal accidents compared to those in other sectors of aviation. To help avoid unplanned weather encounters, accurate information on cloud type and sky conditions can enhance situational awareness and hazard recognition. Among available weather information resources, ground-based webcam networks are growing for aviation meteorology and other interests such as wildfire monitoring. These webcams can provide near-real-time visual weather information to pilots, especially in regions of complex terrain where traditional weather observation methods may lack adequate spatial and temporal coverage. To harness the benefits from webcams while reducing the need for manual interpretation, transfer learning is applied using off-the-shelf convolutional neural networks on a newly constructed meteorological dataset. This dataset, consisting of more than 15500 rigorously labeled images from public webcam networks, is categorized into nine cloud types and weather conditions relevant to general aviation. Leveraging a five-model ensemble approach with the Inception-v3 architecture, a validation accuracy of 97.1% is achieved. Dataset classes are grouped into those that are typically considered nonhazardous or hazardous to general aviation operations, and a hazard-based classification accuracy of 99.5% is attained with the ensemble model. © 2013 IEEE.

First Page

203027

DOI

10.1109/ACCESS.2025.3634057

Publication Date

2025

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