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Fire
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Machine Learning (ML) and Deep Learning (DL) Applications in Wildfire...
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Machine Learning (ML) and Deep Learning (DL) Applications in Wildfire Science: Principles, Progress and Prospects (2nd Edition)

A special issue of Fire (ISSN 2571-6255). This special issue belongs to the section "Fire Science Models, Remote Sensing, and Data".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 3950

Special Issue Editors

Prof. Dr. Washington Franca-Rocha

E-Mail Website
Guest Editor
Department of Sciences, State University of Feira de Santana, Feira de Santana, Brazil
Interests: remote sensing of vegetation; drylands; monitoring degradation through remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. António Vieira

E-Mail Website1 Website2
Guest Editor
Department of Geography, University of Minho, Campus de Azurém, 4810-058 Guimarães, Portugal
Interests: geographic information systems and remote sensing and their application to land use planning; geomorphology; geomorphological heritage; erosive processes following forest fires and mitigation measures
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marcos Francos

E-Mail Website
Guest Editor
1. Historical and Geographical Sciences Department, University of Tarapacá, Arica, Chile 2. Geography Department, University of Barcelona, Barcelona, Spain
Interests: soil; wildfire; prescribed fire; forest management; land-use change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue of Fire entitled “Machine Learning (Ml) and Deep Learning (Dl) Applications in Wildfire Science: Principles, Progress and Prospects”. Artificial Intelligence has been applied in wildfire science for the past 30 years, initially utilizing techniques like neural networks and expert systems. Since then, significant progress has been made, in parallel with the widespread adoption of machine learning (ML) and deep learning (DL) methods in the environmental sciences.

This Special Issue aims to compile the most recent research and advancements in Artificial Intelligence (AI) applications within wildfire science, covering their principles, progress, and prospects. We warmly welcome original research articles, reviews, and perspectives that not only contribute to a better understanding of machine learning (ML) and deep learning (DL) methods among wildfire researchers and managers but also shed light on the diverse and challenging array of problems in wildfire science that can significantly benefit from the expertise of AI data scientists.

Papers cover a wide range of topics of interest utilizing AI, which include, but are not limited to:

  • Fuels characterization, fire detection, and mapping;
  • Fire weather and climate change;
  • Fire occurrence, susceptibility, and risk;
  • Fire behavior prediction;
  • Fire effects;
  • Fire management;
  • Mapping fire extent and severity;
  • Machine learning and big data analytics in wildfire analysis;
  • ML and DL methods;
  • Wildfire prevention and early warning systems;
  • Remote sensing and satellite imagery in wildfire monitoring;
  • Firefighting strategies and technologies;
  • Fire ecology and ecosystem management;
  • Socioeconomic impacts and community resilience to wildfires;
  • Integration of AI and drones for wildfire management;
  • Adaptive management and decision support systems in wildfire response;
  • Fire risk assessment and modeling;
  • Innovative approaches to wildfire suppression and containment;
  • Post-fire rehabilitation and restoration techniques;
  • Multi-agency collaboration and coordination in wildfire management;
  • Wildfire policy, governance, and public awareness efforts.

We look forward to your contribution to this Special Issue in the field of wildfire research and management, which will feature new advances in machine learning and deep learning methods.

This Special Issue is the second edition of “Machine Learning (ML) and Deep Learning (DL) Applications in Wildfire Science: Principles, Progress and Prospects” https://www.mdpi.com/journal/fire/special_issues/36Z3VP118X.

Prof. Dr. Washington Franca-Rocha
Dr. António Vieira
Prof. Dr. Marcos Francos
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. Fire is an international peer-reviewed open access monthly 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

  • wildfire research
  • artificial intelligence applications
  • wildfire risks
  • wildfire modeling
  • data science

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

Related Special Issue

Published Papers (4 papers)

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Research

29 pages, 4292 KB  
Article
A Joint Transformer–XGBoost Model for Satellite Fire Detection in Yunnan
by Luping Dong, Yifan Wang, Chunyan Li, Wenjie Zhu, Haixin Yu and Hai Tian
Fire 2025, 8(10), 376; https://doi.org/10.3390/fire8100376 - 23 Sep 2025
Viewed by 487
Abstract
Wildfires pose a regularly increasing threat to ecosystems and critical infrastructure. The severity of this threat is steadily increasing. The growing threat necessitates the development of technologies for rapid and accurate early detection. However, the prevailing fire point detection algorithms, including several deep [...] Read more.
Wildfires pose a regularly increasing threat to ecosystems and critical infrastructure. The severity of this threat is steadily increasing. The growing threat necessitates the development of technologies for rapid and accurate early detection. However, the prevailing fire point detection algorithms, including several deep learning models, are generally constrained by the inherent hard threshold limitations in their decision-making logic. As a result, these methods lack adaptability and robustness in complex and dynamic real-world scenarios. To address this challenge, the present paper proposes an innovative two-stage, semi-supervised anomaly detection framework. The framework initially employs a Transformer-based autoencoder, which serves to transform raw fire-free time-series data derived from satellite imagery into a multidimensional deep anomaly feature vector. Self-supervised learning achieves this transformation by incorporating both reconstruction error and latent space distance. In the subsequent stage, a semi-supervised XGBoost classifier, trained using an iterative pseudo-labeling strategy, learns and constructs an adaptive nonlinear decision boundary in this high-dimensional anomaly feature space to achieve the final fire point judgment. In a thorough validation process involving multiple real-world fire cases in Yunnan Province, China, the framework attained an F1 score of 0.88, signifying a performance enhancement exceeding 30% in comparison to conventional deep learning baseline models that employ fixed thresholds. The experimental results demonstrate that by decoupling feature learning from classification decision-making and introducing an adaptive decision mechanism, this framework provides a more robust and scalable new paradigm for constructing next-generation high-precision, high-efficiency wildfire monitoring and early warning systems. Full article
(This article belongs to the Special Issue Machine Learning (ML) and Deep Learning (DL) Applications in Wildfire Science: Principles, Progress and Prospects (2nd Edition))
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30 pages, 5405 KB  
Article
A Systematic Machine Learning Methodology for Enhancing Accuracy and Reducing Computational Complexity in Forest Fire Detection
by Marzia Zaman, Darshana Upadhyay, Richard Purcell, Abdul Mutakabbir, Srinivas Sampalli, Chung-Horng Lung and Kshirasagar Naik
Fire 2025, 8(9), 341; https://doi.org/10.3390/fire8090341 - 25 Aug 2025
Viewed by 896
Abstract
Given the critical importance of timely forest fire detection to mitigate environmental and socio-economic consequences, this research aims to achieve high detection accuracy while maintaining real-time operational efficiency, with a particular focus on minimizing computational complexity. We propose a novel framework that systematically [...] Read more.
Given the critical importance of timely forest fire detection to mitigate environmental and socio-economic consequences, this research aims to achieve high detection accuracy while maintaining real-time operational efficiency, with a particular focus on minimizing computational complexity. We propose a novel framework that systematically integrates normalization, feature selection, adaptive oversampling, and classifier optimization to enhance detection performance while minimizing computational overhead. The evaluation is conducted using three distinct Canadian forest fire datasets: Alberta Forest Fire (AFF), British Columbia Forest Fire (BCFF), and Saskatchewan Forest Fire (SFF). Initial classifier benchmarking identified the best-performing tree-based model, followed by normalization and feature selection optimization. Next, four oversampling methods were evaluated to address class imbalance. An ablation study quantified the contribution of each module to overall performance. Our targeted, stepwise strategy eliminated the need for exhaustive model searches, reducing computational cost by 97.75% without compromising accuracy. Experimental results demonstrate substantial improvements in F1-score, AFF (from 69.12% to 82.75%), BCFF (61.95% to 77.91%), and SFF (90.03% to 96.18%) alongside notable reductions in False Negative Rates compared to baseline models. Full article
(This article belongs to the Special Issue Machine Learning (ML) and Deep Learning (DL) Applications in Wildfire Science: Principles, Progress and Prospects (2nd Edition))
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18 pages, 2701 KB  
Article
YOLOv11-CHBG: A Lightweight Fire Detection Model
by Yushuang Jiang, Peisheng Liu, Yunping Han and Bei Xiao
Fire 2025, 8(9), 338; https://doi.org/10.3390/fire8090338 - 24 Aug 2025
Viewed by 1070
Abstract
Fire is a disaster that seriously threatens people’s lives. Because fires occur suddenly and spread quickly, especially in densely populated places or areas where it is difficult to evacuate quickly, it often causes major property damage and seriously endangers personal safety. Therefore, it [...] Read more.
Fire is a disaster that seriously threatens people’s lives. Because fires occur suddenly and spread quickly, especially in densely populated places or areas where it is difficult to evacuate quickly, it often causes major property damage and seriously endangers personal safety. Therefore, it is necessary to detect the occurrence of fires accurately and promptly and issue early warnings. This study introduces YOLOv11-CHBG, a novel detection model designed to identify flames and smoke. On the basis of YOLOv11, the C3K2-HFERB module is used in the backbone part, the BiAdaGLSA module is proposed in the neck, the SEAM attention mechanism is added to the model detection head, and the proposed model is more lightweight, offering potential support for fire rescue efforts. The model developed in this study is shown by the experimental results to achieve an average precision (mAP@0.5) of 78.4% on the Dfire datasets, with a 30.8% reduction in parameters compared to YOLOv11. The model achieves a lightweight design, enhancing its significance for real-time fire and smoke detection, and it provides a research basis for detecting fires earlier, preventing the spread of fires and reducing the harm caused by fires. Full article
(This article belongs to the Special Issue Machine Learning (ML) and Deep Learning (DL) Applications in Wildfire Science: Principles, Progress and Prospects (2nd Edition))
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23 pages, 6067 KB  
Article
Daily-Scale Fire Risk Assessment for Eastern Mongolian Grasslands by Integrating Multi-Source Remote Sensing and Machine Learning
by Risu Na, Byambakhuu Gantumur, Wala Du, Sainbuyan Bayarsaikhan, Yu Shan, Qier Mu, Yuhai Bao, Nyamaa Tegshjargal and Battsengel Vandansambuu
Fire 2025, 8(7), 273; https://doi.org/10.3390/fire8070273 - 11 Jul 2025
Viewed by 1232
Abstract
Frequent wildfires in the eastern grasslands of Mongolia pose significant threats to the ecological environment and pastoral livelihoods, creating an urgent need for high-temporal-resolution and high-precision fire prediction. To address this, this study established a daily-scale grassland fire risk assessment framework integrating multi-source [...] Read more.
Frequent wildfires in the eastern grasslands of Mongolia pose significant threats to the ecological environment and pastoral livelihoods, creating an urgent need for high-temporal-resolution and high-precision fire prediction. To address this, this study established a daily-scale grassland fire risk assessment framework integrating multi-source remote sensing data to enhance predictive capabilities in eastern Mongolia. Utilizing fire point data from eastern Mongolia (2012–2022), we fused multiple feature variables and developed and optimized three models: random forest (RF), XGBoost, and deep neural network (DNN). Model performance was enhanced using Bayesian hyperparameter optimization via Optuna. Results indicate that the Bayesian-optimized XGBoost model achieved the best generalization performance, with an overall accuracy of 92.3%. Shapley additive explanations (SHAP) interpretability analysis revealed that daily-scale meteorological factors—daily average relative humidity, daily average wind speed, daily maximum temperature—and the normalized difference vegetation index (NDVI) were consistently among the top four contributing variables across all three models, identifying them as key drivers of fire occurrence. Spatiotemporal validation using historical fire data from 2023 demonstrated that fire points recorded on 8 April and 1 May 2023 fell within areas predicted to have “extremely high” fire risk probability on those respective days. Moreover, points A (117.36° E, 46.70° N) and B (116.34° E, 49.57° N) exhibited the highest number of days classified as “high” or “extremely high” risk during the April/May and September/October periods, consistent with actual fire occurrences. In summary, the integration of multi-source data fusion and Bayesian-optimized machine learning has enabled the first high-precision daily-scale wildfire risk prediction for the eastern Mongolian grasslands, thus providing a scientific foundation and decision-making support for wildfire prevention and control in the region. Full article
(This article belongs to the Special Issue Machine Learning (ML) and Deep Learning (DL) Applications in Wildfire Science: Principles, Progress and Prospects (2nd Edition))
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