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Odusina, T, Perkasa, P, Chalmers, C, Fergus, P, Longmore, SN and Wich, S (2025) Detection and Geolocation of Peat Fires Using Thermal Infrared Cameras on Drones. Drones, 9 (7).
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Abstract
Peat fires are a major hazard to human and animal health and can negatively impact livelihoods. Once peat fires start to burn, they are difficult to extinguish and can continue to burn for months, destroying biomass and contributing to carbon emissions globally. In areas with limited accessibility and periods of thick haze and fog, these fires are difficult to detect, localize, and tackle. To address this problem, thermal infrared cameras mounted on drones can provide a potential solution since they allow large areas to be surveyed relatively quickly and can detect thermal radiation from fires above and below the peat surface. This paper describes a deep learning pipeline that detects and segments peat fires in thermal images. Controlled peat fires were constructed under varying environmental conditions and thermal images were taken to form a dataset for our pipeline. A semi-automated approach was adopted to label images using Otsu’s adaptive thresholding technique, which significantly reduces the required effort often needed to tag objects in images. The proposed method uses a pre-trained ResNet-50 model as a backbone (encoder) for feature extraction and is augmented with a set of up-sampling layers and skip connections, like the UNet architecture. The experimental results show that the model can achieve an IOU score of 87.6% on an unseen test set of thermal images containing peat fires. In comparison, a MobileNetV2 model trained under the same experimental conditions achieved an IOU score of 57.9%. In addition, the model is robust to false positives, which is indicated by a precision equal to 94.2%. To demonstrate its practical utility, the model was also tested on real peat wildfires, and the results are promising, as indicated by a high IOU score of 90%. Finally, a geolocation algorithm is presented to identify the GNSS location of these fires once they are detected in an image to aid fire-fighting responses. The proposed scheme was built using a web-based platform that performs offline detection and allows peat fires to be geolocated.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 40 Engineering; 46 Information and computing sciences |
| Subjects: | G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Astrophysics Research Institute Biological and Environmental Sciences (from Sep 19) Computer Science and Mathematics |
| Publisher: | MDPI |
| Related URLs: | |
| Date of acceptance: | 4 June 2025 |
| Date of first compliant Open Access: | 25 June 2025 |
| Date Deposited: | 25 Jun 2025 14:41 |
| Last Modified: | 25 Jun 2025 14:45 |
| DOI or ID number: | 10.3390/drones9070459 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/26656 |
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