Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Advanced Approaches in Fire Detection and Prediction
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advanced Approaches in Fire Detection and Prediction

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (25 January 2022) | Viewed by 4597

Special Issue Editors

Dr. Moez Bouchouicha

E-Mail Website
Guest Editor
LIS-CNRS, Université de Toulon, Aix-Marseille University, 83041 Toulon, France
Interests: computer vision; AI; deep learning; medical imaging; fire detection
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Eric Moreau

E-Mail Website
Guest Editor
Aix Marseille Univ, Université de Toulon, CNRS, LIS, 83041 Toulon, France
Interests: signal processing; independent component analysis; source separation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last decade, fires have killed hundreds of people and ravaged forests all over the world. In addition to the socioeconomic impact in terms of loss of human lives and first responders, health, infrastructure, and economic activity, fire events also have serious and irreversible ecological impacts when considering soil degradation, water scarcity, and biodiversity loss.

This Special Issue will propose innovative approaches tailored to fire risk detection. Papers are invited that investigate more advanced techniques, models, solutions, and capabilities for preventing, predicting, monitoring fire risk, including advanced technologies, equipment, and decision support systems for first responders. Topics may include computer vision methods for fire and/or smoke detection via visible, IR, and multi-spectral range of ground and/or aerial videos/images. Moreover, papers are welcome that deal with fire propagation forecasting, thanks to models based on precise topography, weather, fuel, and combustion modeling via sensor data.

Dr. Moez Bouchouicha
Prof. Dr. Eric Moreau
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fire smoke detection
  • computer vision
  • image processing
  • machine learning
  • fire modeling
  • multispectral imaging

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

28 pages, 10210 KiB  
Article
Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation
by Jorge Francisco Ciprián-Sánchez, Gilberto Ochoa-Ruiz, Lucile Rossi and Frédéric Morandini
Appl. Sci. 2021, 11(15), 7046; https://doi.org/10.3390/app11157046 - 30 Jul 2021
Cited by 12 | Viewed by 3408
Abstract
Wildfires stand as one of the most relevant natural disasters worldwide, particularly more so due to the effect of climate change and its impact on various societal and environmental levels. In this regard, a significant amount of research has been done in order [...] Read more.
Wildfires stand as one of the most relevant natural disasters worldwide, particularly more so due to the effect of climate change and its impact on various societal and environmental levels. In this regard, a significant amount of research has been done in order to address this issue, deploying a wide variety of technologies and following a multi-disciplinary approach. Notably, computer vision has played a fundamental role in this regard. It can be used to extract and combine information from several imaging modalities in regard to fire detection, characterization and wildfire spread forecasting. In recent years, there has been work pertaining to Deep Learning (DL)-based fire segmentation, showing very promising results. However, it is currently unclear whether the architecture of a model, its loss function, or the image type employed (visible, infrared, or fused) has the most impact on the fire segmentation results. In the present work, we evaluate different combinations of state-of-the-art (SOTA) DL architectures, loss functions, and types of images to identify the parameters most relevant to improve the segmentation results. We benchmark them to identify the top-performing ones and compare them to traditional fire segmentation techniques. Finally, we evaluate if the addition of attention modules on the best performing architecture can further improve the segmentation results. To the best of our knowledge, this is the first work that evaluates the impact of the architecture, loss function, and image type in the performance of DL-based wildfire segmentation models. Full article
(This article belongs to the Special Issue Advanced Approaches in Fire Detection and Prediction)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop