Deep Learning Based Wildfire Detection for Peatland Fires Using Transfer Learning
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915830556524544 |
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| author | Hamdan, Emadeldeen Tharima, Ahmad Faiz Tohir, Mohd Zahirasri Mohd Musa, Dayang Nur Sakinah Koyuncu, Erdem Watts, Adam J. Cetin, Ahmet Enis |
| author_facet | Hamdan, Emadeldeen Tharima, Ahmad Faiz Tohir, Mohd Zahirasri Mohd Musa, Dayang Nur Sakinah Koyuncu, Erdem Watts, Adam J. Cetin, Ahmet Enis |
| contents | Machine learning (ML)-based wildfire detection methods have been developed in recent years, primarily using deep learning (DL) models trained on large collections of wildfire images and videos. However, peatland fires exhibit distinct visual and physical characteristics -- such as smoldering combustion, low flame intensity, persistent smoke, and subsurface burning -- that limit the effectiveness of conventional wildfire detectors trained on open-flame forest fires. In this work, we present a transfer learning-based approach for peatland fire detection that leverages knowledge learned from general wildfire imagery and adapts it to the peatland fire domain. We initialize a DL-based peatland fire detector using pretrained weights from a conventional wildfire detection model and subsequently fine-tune the network using a dataset composed of Malaysian peatland images and videos. This strategy enables effective learning despite the limited availability of labeled peatland fire data. Experimental results demonstrate that transfer learning significantly improves detection accuracy and robustness compared to training from scratch, particularly under challenging conditions such as low-contrast smoke, partial occlusions, and variable illumination. The proposed approach provides a practical and scalable solution for early peatland fire detection and has the potential to support real-time monitoring systems for fire prevention and environmental protection. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_02465 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep Learning Based Wildfire Detection for Peatland Fires Using Transfer Learning Hamdan, Emadeldeen Tharima, Ahmad Faiz Tohir, Mohd Zahirasri Mohd Musa, Dayang Nur Sakinah Koyuncu, Erdem Watts, Adam J. Cetin, Ahmet Enis Computer Vision and Pattern Recognition Artificial Intelligence Machine learning (ML)-based wildfire detection methods have been developed in recent years, primarily using deep learning (DL) models trained on large collections of wildfire images and videos. However, peatland fires exhibit distinct visual and physical characteristics -- such as smoldering combustion, low flame intensity, persistent smoke, and subsurface burning -- that limit the effectiveness of conventional wildfire detectors trained on open-flame forest fires. In this work, we present a transfer learning-based approach for peatland fire detection that leverages knowledge learned from general wildfire imagery and adapts it to the peatland fire domain. We initialize a DL-based peatland fire detector using pretrained weights from a conventional wildfire detection model and subsequently fine-tune the network using a dataset composed of Malaysian peatland images and videos. This strategy enables effective learning despite the limited availability of labeled peatland fire data. Experimental results demonstrate that transfer learning significantly improves detection accuracy and robustness compared to training from scratch, particularly under challenging conditions such as low-contrast smoke, partial occlusions, and variable illumination. The proposed approach provides a practical and scalable solution for early peatland fire detection and has the potential to support real-time monitoring systems for fire prevention and environmental protection. |
| title | Deep Learning Based Wildfire Detection for Peatland Fires Using Transfer Learning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2603.02465 |