The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Metadata Harvesting (OAI2)
  • Digital Archiving Policy
  • Promote your Publication

IJACSA

  • About the Journal
  • Call for Papers
  • Author Guidelines
  • Fees/ APC
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Guidelines
  • Fees
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Subscribe

Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Forest Fires Detection using Deep Transfer Learning

Author 1: Mimoun YANDOUZI
Author 2: Mounir GRARI
Author 3: Idriss IDRISSI
Author 4: Mohammed BOUKABOUS
Author 5: Omar MOUSSAOUI
Author 6: Mostafa AZIZI
Author 7: Kamal GHOUMID
Author 8: Aissa KERKOUR ELMIAD

Download PDF

Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130832

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 8, 2022.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Forests are vital ecosystems composed of various plant and animal species that have evolved over years to coexist. Such ecosystems are often threatened by wildfires that can start either naturally, as a result of lightning strikes, or unintentionally caused by humans. In general, human-caused fires are more severe and expensive to fight because they are frequently located in inaccessible areas. Wildfires can spread quickly and become extremely dangerous, causing damage to homes and facilities, as well as killing people and animals. Early discovery of wildfires is vital to protect lives, property, and resources. Reinforced imaging technologies can play a key role to detect wildfires earlier. By applying deep learning (DL) over a dataset of images (collected using drones, planes, and satellites), we target to automate the forest fire detection. In this paper, we focus on building a DL model specifically to detect wildfires using transfer learning techniques from the best pretrained DL computer vision architectures available nowadays, such as VGG16, VGG19, Inceptionv3, ResNet50, ResNet50V2, InceptionResNetV2, Xception, Dense-Net, MobileNet, MobileNetV2, and NASNetMobile. Our proposed approach attained a detection rate of more than 99.9% over multiple metrics, proving that it could be used in real-world forest fire detection applications.

Keywords: Forest fires; wildfires; deep learning; transfer learning; computer vision; convolutional neural networks (CNN)

Mimoun YANDOUZI, Mounir GRARI, Idriss IDRISSI, Mohammed BOUKABOUS, Omar MOUSSAOUI, Mostafa AZIZI, Kamal GHOUMID and Aissa KERKOUR ELMIAD, “Forest Fires Detection using Deep Transfer Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130832

@article{YANDOUZI2022,
title = {Forest Fires Detection using Deep Transfer Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130832},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130832},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Mimoun YANDOUZI and Mounir GRARI and Idriss IDRISSI and Mohammed BOUKABOUS and Omar MOUSSAOUI and Mostafa AZIZI and Kamal GHOUMID and Aissa KERKOUR ELMIAD}
}


IJACSA

Upcoming Conferences

Future of Information and Communication Conference (FICC) 2023

2-3 March 2023

  • Virtual

Computing Conference 2023

22-23 June 2023

  • London, United Kingdom

IntelliSys 2023

7-8 September 2023

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2023

2-3 November 2023

  • San Francisco, United States
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. Registered in England and Wales. Company Number 8933205. All rights reserved. thesai.org