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Deep Learning Based Wildfire Detection for Peatland Fires Using Transfer Learning

arXiv – CS AI|Emadeldeen Hamdan, Ahmad Faiz Tharima, Mohd Zahirasri Mohd Tohir, Dayang Nur Sakinah Musa, Erdem Koyuncu, Adam J. Watts, Ahmet Enis Cetin|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers developed a transfer learning approach for detecting peatland fires using deep learning models adapted from conventional wildfire detection systems. The method addresses the unique challenges of peatland fires, which have distinct characteristics like low flame intensity and persistent smoke that make them difficult to detect with standard wildfire detection models.

Key Takeaways

→Transfer learning approach adapts existing wildfire detection models specifically for peatland fire detection.
→Peatland fires have unique characteristics like smoldering combustion and subsurface burning that challenge conventional detectors.
→The method uses pretrained weights from wildfire models and fine-tunes on Malaysian peatland fire datasets.
→Transfer learning significantly outperformed training from scratch, especially in challenging conditions.
→The approach offers potential for real-time monitoring systems for environmental protection.

#deep-learning #transfer-learning #wildfire-detection #computer-vision #environmental-ai #machine-learning #fire-detection

Read Original →via arXiv – CS AI

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