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A UAV-Based Ignition Detection in Prescribed Fires Using Deep Learning †

Centre of Technology and Systems—Instituto de Desenvolvimento de Novas Tecnologias (UNINOVA-CTS), FCT Campus, 2829-516 Caparica, Portugal
Author to whom correspondence should be addressed.
Presented at the Third International Conference on Fire Behavior and Risk, Sardinia, Italy, 3–6 May 2022.
Environ. Sci. Proc. 2022, 17(1), 59;
Published: 10 August 2022
(This article belongs to the Proceedings of The Third International Conference on Fire Behavior and Risk)


Forest wildfires have been an aggravating disaster during the past decades due to the rise of global temperatures. Forest management operations like prescribed fires are paramount for preserving these environments, although they present intrinsic risks. The FoCoR project aims to exploit UAVs with multispectral cameras to help manage prescribed fires by applying real-time detection and segmentation of ignitions. This paper proposes and details a basic supervised Deep Learning model capable of accurately detecting and segmenting prescribed fires. The model is based on the Mask R-CNN framework and is optimized with the best f1-score of approximately 70% which was considered a good starting point for further development. The used image dataset, consisting of more than 2500 polygonal labeled aerial RGB images acquired during prescribed fires will also be made publicly available for training more models in the future.

Author Contributions

Conceptualization F.M. and L.H.; methodology, F.M. and L.H.; software, L.H., I.O. and N.G.; validation, all authors; project administration, F.M. and J.B.; funding acquisition, J.B. All authors have read and agreed to the published version of the manuscript.


This work was supported by FoCoR project PCIF/MPG/0086/2017 funded by FCT—Portuguese Foundation for Science and Technology.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.
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Share and Cite

MDPI and ACS Style

Marques, F.; Herranz, L.; Ghalati, N.; Oliveira, I.; Barata, J. A UAV-Based Ignition Detection in Prescribed Fires Using Deep Learning. Environ. Sci. Proc. 2022, 17, 59.

AMA Style

Marques F, Herranz L, Ghalati N, Oliveira I, Barata J. A UAV-Based Ignition Detection in Prescribed Fires Using Deep Learning. Environmental Sciences Proceedings. 2022; 17(1):59.

Chicago/Turabian Style

Marques, Francisco, Lucas Herranz, Nastaran Ghalati, Inês Oliveira, and Jose Barata. 2022. "A UAV-Based Ignition Detection in Prescribed Fires Using Deep Learning" Environmental Sciences Proceedings 17, no. 1: 59.

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