Vol. 5 No. 3 (2026): July
LITERATURE / REVIEW ARTICLE

Multi-Platform Forest Fire Detection Using Deep Learning and IoT: A Review

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  • Nikhil Kumar,
  • Vinay Kumar,
  • Sourav,
  • Mayank Chopra,
  • Parveen Sadotra,
  • Pradeep Chouksey
Nikhil Kumar
Central University of Himachal Pradesh, Shahpur Parisar, Kangra, Himachal Pradesh, India
Vinay Kumar
Central University of Himachal Pradesh
Sourav
Central University of Himachal Pradesh
Mayank Chopra
Central University of Himachal Pradesh
Parveen Sadotra
Central University of Himachal Pradesh
Pradeep Chouksey
Central University of Himachal Pradesh
DOI: https://doi.org/10.5281/zenodo.20056355

Published 2026-05-06

Keywords

  • forest fire detection, deep learning, IoT, UAV, sensor fusion, edge computing, satellite imagery, LULC analysis, CNN-ViT hybrid, YOLO.

How to Cite

Kumar, N., Kumar, V., Sourav, Chopra, M., Sadotra, P., & Chouksey, P. (2026). Multi-Platform Forest Fire Detection Using Deep Learning and IoT: A Review. International Journal of Computational Learning & Intelligence, 5(3), 1017–1031. https://doi.org/10.5281/zenodo.20056355

Copyright (c) 2026 Nikhil Kumar, Vinay Kumar, Sourav, Mayank Chopra, Parveen Sadotra, Pradeep Chouksey

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Forest fires have severe impacts on ecosystems and property, but current detection methods continue to suffer from detection latency, coverage and environmental limitations. In this paper, we review the state of the art in integrated forest fire detection systems that leverage deep learning, Internet of Things (IoT) sensor networks, unmanned aerial vehicle (UAV) monitoring, and satellite remote sensing. Over two dozen recent works are reviewed to discuss object detection algorithms, hybrid deep learning models, sensor fusion techniques, satellite remote sensing, and land use/land cover (LULC) change analyses for predictive fire risk mapping. Key research challenges are identified in the areas of integration, data, environmental adaptability, efficiency, and the use of contextual information. Popular benchmark datasets, performance metrics and system characteristics are also presented. Based on this review, a visionary research proposal is provided detailing the design and approach for developing a holistic multi-platform detection system to achieve detection within 5 minutes with an accuracy of more than 95% and false alarm rate of less than 5% in different ecosystems.

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