Multispectral deep learning models for wildfire detection

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Authors
Haridasan, Smitha
Rattani, Ajita
Demissie, Zelalem
Dutta, Atri
Advisors
Issue Date
2022-07-06
Type
Conference paper
Keywords
Deep learning , Forest fire detection , Natural hazard detection , Multi-spectral learning
Research Projects
Organizational Units
Journal Issue
Citation
Haridasan, S., Rattani, A., Demissie, Z., & Dutta, A. (2022). Multispectral deep learning models for wildfire detection Data-driven Resilience Research 2022, Leipzig, Germany.
Abstract

Aided by wind, all it takes is one ember and few minutes to create wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision based techniques have been proposed for early detection of forest fire using video surveillance. Several computer vision based methods have been proposed to predict and detect forest fires at various spectrum, namely, RGB, HSV and YCbCr. The aim of this paper is to propose multi-spectral deep learning model that combine information from different spectrum at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available dataset is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 1.9% and 14.88% in test and challenge set over those based on single spectrum for fire detection even in challenging environments.

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Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Publisher
CEUR-WS
Journal
Book Title
Series
CEUR Workshop Proceedings
PubMed ID
DOI
ISSN
1613-0073
EISSN