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Vision Transformer-Based Forest Fire Classification: Wild Life Management System

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Artificial Intelligence for Sustainable Development

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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Abstract

One of the natural resources in the world is forest, because the ecosystems are directly influenced by the forest area. So, to preserve the strong and blossoming ecosystem, forest maintenance is undoubtably necessary. In recent time, especially at summer season, forest fire is one of the serious problems. This forest may collapse the eco system. So early detection of fire in forest area is important. The computer vision with artificial intelligence is one of the successful research areas, which is still providing exact solutions for many problems. Accordingly, this chapter proposed Vision Transformer Model (ViTM) for early forest fire detection. The performance of the proposed system is analyzed with various performance metrics successfully. The planned early fire detection model produced 98.7% accuracy on the classification of fire and no-fire dataset. From the result, we can conclude that the proposed ViTM is an exclusive fire detection model.

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Authors and Affiliations

  1. Centre for Future Networks and Digital Twin, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Anandakumar Haldorai

  2. Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Babitha Lincy R, Suriya Murugan & Minu Balakrishnan

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  1. Anandakumar Haldorai
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  2. Babitha Lincy R
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  3. Suriya Murugan
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  4. Minu Balakrishnan
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Haldorai, A., R, B.L., Murugan, S., Balakrishnan, M. (2024). Vision Transformer-Based Forest Fire Classification: Wild Life Management System. In: Artificial Intelligence for Sustainable Development. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-53972-5_24

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  • DOI: https://doi.org/10.1007/978-3-031-53972-5_24

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