Summary of Utilizing Transfer Learning and Pre-trained Models For Effective Forest Fire Detection: a Case Study Of Uttarakhand, by Hari Prabhat Gupta and Rahul Mishra
Utilizing Transfer Learning and pre-trained Models for Effective Forest Fire Detection: A Case Study of Uttarakhand
by Hari Prabhat Gupta, Rahul Mishra
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This research paper explores the application of transfer learning to improve forest fire detection in India, addressing challenges posed by regional differences in terrain, climate, and vegetation. The study compares traditional learning methods with transfer learning, highlighting the benefits of using pre-trained models like MobileNetV2 for efficient data adaptation. Experimental results demonstrate the effectiveness of this approach using the Uttarakhand forest fire dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Forest fires are a significant threat to the environment, human life, and property. Early detection is crucial, but traditional methods rely on manual observation and low-resolution satellite imagery. This paper shows how transfer learning can help detect forest fires in India by adapting pre-trained models like MobileNetV2 for specific tasks. |
Keywords
» Artificial intelligence » Transfer learning
