Summary of A Multi-level Hierarchical Framework For the Classification Of Weather Conditions and Hazard Prediction, by Harish Neelam
A Multi-Level Hierarchical Framework for the Classification of Weather Conditions and Hazard Prediction
by Harish Neelam
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A multilevel hierarchical framework is proposed for classifying weather conditions and predicting hazards. This framework can classify images into 11 categories: dew, frost, glaze, rime, snow, hail, rain, lightning, rainbow, sandstorm, and fog-free conditions. The model’s accuracy is crucial in real-life situations to prevent accidents, making it the top priority. The framework lays the groundwork for future applications in weather prediction, especially in situations where human expertise is not available or may be biased. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict the weather using pictures is developed. This helps with traffic management, afforestation, and weather forecasting. It’s especially important when traditional weather predictions are not accurate enough, like ensuring self-driving cars stay safe in bad weather. The goal is to create a model that can correctly predict the weather after being trained on many images. The framework has an accuracy of 0.9329 for predicting real-time weather information. |
