{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T16:40:13Z","timestamp":1776703213529,"version":"3.51.2"},"reference-count":58,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T00:00:00Z","timestamp":1714608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Sciences and Engineering Research Council of Canada (NSERC)","award":["RGPIN-2018-06233"],"award-info":[{"award-number":["RGPIN-2018-06233"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Wildland fires cause economic and ecological damage with devastating consequences, including loss of life. To reduce these risks, numerous fire detection and recognition systems using deep learning techniques have been developed. However, the limited availability of annotated datasets has decelerated the development of reliable deep learning techniques for detecting and monitoring fires. For such, a novel dataset, namely, SWIFT, is presented in this paper for detecting and recognizing wildland smoke and fires. SWIFT includes a large number of synthetic images and videos of smoke and wildfire with their corresponding annotations, as well as environmental data, including temperature, humidity, wind direction, and speed. It represents various wildland fire scenarios collected from multiple viewpoints, covering forest interior views, views near active fires, ground views, and aerial views. In addition, three deep learning models, namely, BoucaNet, DC-Fire, and CT-Fire, are adopted to recognize forest fires and address their related challenges. These models are trained using the SWIFT dataset and tested using real fire images. BoucaNet performed well in recognizing wildland fires and overcoming challenging limitations, including the complexity of the background, the variation in smoke and wildfire features, and the detection of small wildland fire areas. This shows the potential of sim-to-real deep learning in wildland fires.<\/jats:p>","DOI":"10.3390\/rs16091627","type":"journal-article","created":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T03:57:56Z","timestamp":1714622276000},"page":"1627","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["SWIFT: Simulated Wildfire Images for Fast Training Dataset"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4650-6536","authenticated-orcid":false,"given":"Luiz","family":"Fernando","sequence":"first","affiliation":[{"name":"Perception, Robotics and Intelligent Machines (PRIME), Department of Computer Science, Universit\u00e9 de Moncton, Moncton, NB E1A 3E9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9645-2452","authenticated-orcid":false,"given":"Rafik","family":"Ghali","sequence":"additional","affiliation":[{"name":"Perception, Robotics and Intelligent Machines (PRIME), Department of Computer Science, Universit\u00e9 de Moncton, Moncton, NB E1A 3E9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4378-2669","authenticated-orcid":false,"given":"Moulay A.","family":"Akhloufi","sequence":"additional","affiliation":[{"name":"Perception, Robotics and Intelligent Machines (PRIME), Department of Computer Science, Universit\u00e9 de Moncton, Moncton, NB E1A 3E9, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,2]]},"reference":[{"key":"ref_1","unstructured":"Jones, M.W., Smith, A., Betts, R., Canadell, J.G., Prentice, I.C., and Qu\u00e9r\u00e9, C.L. (2024, March 12). Climate Change Increases the Risk of Wildfires. Available online: https:\/\/sciencebrief.org\/briefs\/wildfires."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19167","DOI":"10.1073\/pnas.1003669107","article-title":"Driving Forces of Global Wildfires over the Past Millennium and the Forthcoming Century","volume":"107","author":"Pechony","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8517","DOI":"10.1029\/2019GL083699","article-title":"Climate Change Increases the Potential for Extreme Wildfires","volume":"46","author":"Evans","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_4","unstructured":"Natural Resources Canada (2024, March 11). National Wildland Fire Situation Report. Available online: https:\/\/cwfis.cfs.nrcan.gc.ca\/report."},{"key":"ref_5","unstructured":"European Commission (2024, March 11). 2022 Was the Second-Worst Year for Wildfires. Available online: https:\/\/ec.europa.eu\/commission\/presscorner\/detail\/en\/ip_23_5951."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Thomas, D.S., Butry, D.T., Gilbert, S.W., Webb, D.H., and Fung, J.F. (2017). The Costs and Losses of Wildfires, NIST Special Publication.","DOI":"10.6028\/NIST.SP.1215"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1289\/ehp.1409277","article-title":"Critical Review of Health Impacts of Wildfire Smoke Exposure","volume":"124","author":"Reid","year":"2016","journal-title":"Environ. Health Perspect."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ghali, P., and Akhloufi, M.A. (2023). Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction. Fire, 6.","DOI":"10.3390\/fire6050192"},{"key":"ref_9","first-page":"101152","article-title":"Learning Class-specific Spectral Patterns to Improve Deep Learning-based Scene-level Fire Smoke Detection from Multi-spectral Satellite Imagery","volume":"34","author":"Zhao","year":"2024","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ghali, P., and Akhloufi, M.A. (2023). Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation. Remote Sens., 15.","DOI":"10.3390\/rs15071821"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108309","DOI":"10.1016\/j.sigpro.2021.108309","article-title":"A Review on Early Wildfire Detection from Unmanned Aerial Vehicles using Deep Learning-based Computer Vision Algorithms","volume":"190","author":"Bouguettaya","year":"2022","journal-title":"Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"121301","DOI":"10.1109\/ACCESS.2022.3222805","article-title":"Wildland Fire Detection and Monitoring using a Drone-Collected RGB\/IR Image Dataset","volume":"10","author":"Chen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jin, L., Yu, Y., Zhou, J., Bai, D., Lin, H., and Zhou, H. (2024). SWVR: A Lightweight Deep Learning Algorithm for Forest Fire Detection and Recognition. Forests, 15.","DOI":"10.3390\/f15010204"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"121665","DOI":"10.1016\/j.eswa.2023.121665","article-title":"FSDF: A High-performance Fire Detection Framework","volume":"238","author":"Zhao","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ghali, R., and Akhloufi, M.A. (2023, January 21\u201325). Wildfires Detection and Segmentation Using Deep CNNs and Vision Transformers. Proceedings of the Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges, Montreal, QC, Canada.","DOI":"10.1007\/978-3-031-37742-6_19"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1186\/s40537-023-00727-2","article-title":"A survey on Deep Learning Ttools Dealing with Data Scarcity: Definitions, Challenges, Solutions, Tips, and Applications","volume":"10","author":"Alzubaidi","year":"2023","journal-title":"J. Big Data"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chino, D.Y.T., Avalhais, L.P.S., Rodrigues, J.F., and Traina, A.J.M. (2015, January 26\u201329). BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis. Proceedings of the 28th SIBGRAPI Conference on Graphics, Patterns and Images, Salvador, Brazil.","DOI":"10.1109\/SIBGRAPI.2015.19"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.firesaf.2017.06.012","article-title":"Computer Vision for Wildfire Research: An Evolving Image Dataset for Processing and Analysis","volume":"92","author":"Toulouse","year":"2017","journal-title":"Fire Saf. J."},{"key":"ref_19","first-page":"5358359","article-title":"DeepFire: A Novel Dataset and Deep Transfer Learning Benchmark for Forest Fire Detection","volume":"2022","author":"Khan","year":"2022","journal-title":"Mob. Inf. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108001","DOI":"10.1016\/j.comnet.2021.108001","article-title":"Aerial Imagery Pile Burn Detection using Deep Learning: The FLAME dataset","volume":"193","author":"Shamsoshoara","year":"2021","journal-title":"Comput. Netw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1214\/13-STS451","article-title":"Wildfire Prediction to Inform Fire Management: Statistical Science Challenges","volume":"28","author":"Taylor","year":"2013","journal-title":"Stat. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1038\/s41597-019-0312-2","article-title":"A global wildfire dataset for the analysis of fire regimes and fire behaviour","volume":"6","author":"Oom","year":"2019","journal-title":"Sci. Data"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Singh, M., Fuenmayor, E., Hinchy, E.P., Qiao, Y., Murray, N., and Devine, D. (2021). Digital Twin: Origin to Future. Appl. Syst. Innov., 4.","DOI":"10.3390\/asi4020036"},{"key":"ref_24","unstructured":"Koenig, L., Nowicki, A., Montgomery, L.N., and Lordan, D. (2022, January 12\u201316). Machine Learning within the Cognitive Mission Manger for Wildland Fire Management. Proceedings of the AGU Fall Meeting Abstracts, Chicago, IL, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7390","DOI":"10.1080\/01431161.2023.2283904","article-title":"CT-Fire: A CNN-Transformer for wildfire classification on ground and aerial images","volume":"44","author":"Ghali","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ghali, R., and Akhlouf, M.A. (2023). BoucaNet: A CNN-Transformer for Smoke Recognition on Remote Sensing Satellite Images. Fire, 6.","DOI":"10.3390\/fire6120455"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ghali, R., and Akhlouf, M.A. (November, January 30). DC-Fire: A Deep Convolutional Neural Network for Wildland Fire Recognition on Aerial Infrared Images. Proceedings of the fourth Quantitative Infrared Thermography Asian Conference (QIRT-Asia 2023), Abu Dhabi, United Arab Emirates.","DOI":"10.21611\/qirt.2023.03"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1038\/s41893-019-0451-7","article-title":"Barriers and Enablers for Prescribed burns for Wildfire Management in California","volume":"3","author":"Miller","year":"2020","journal-title":"Nat. Sustain."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shah, S., Dey, D., Lovett, C., and Kapoor, A. (2017, January 12\u201315). AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles. Proceedings of the Field and Service Robotics, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-67361-5_40"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bhattarai, M., and Martinez-Ramon, M. (2021, January 4\u20136). A Deep Q-learning based Path Planning and Navigation System for Firefighting Environments. Proceedings of the 13th International Conference on Agents and Artificial Intelligence, Virtual Event.","DOI":"10.5220\/0010267102670277"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ma, C., Zhou, Y., and Li, Z. (2020, January 20\u201323). A New Simulation Environment Based on Airsim, ROS, and PX4 for Quadcopter Aircrafts. Proceedings of the 6th International Conference on Control, Automation and Robotics (ICCAR), Singapore.","DOI":"10.1109\/ICCAR49639.2020.9108103"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1111\/cgf.12282","article-title":"Inverse Procedural Modelling of Trees","volume":"33","author":"Stava","year":"2014","journal-title":"Comput. Graph. Forum"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3306346.3323039","article-title":"Synthetic Silviculture: Multi-Scale Modeling of Plant Ecosystems","volume":"38","author":"Makowski","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3083725","article-title":"Interactive Wood Combustion for Botanical Tree Models","volume":"36","author":"Pirk","year":"2017","journal-title":"ACM Trans. Graph."},{"key":"ref_35","first-page":"1","article-title":"Fire in Paradise: Mesoscale Simulation of Wildfires","volume":"40","author":"Banuti","year":"2021","journal-title":"ACM Trans. Graph."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ni\u021b\u0103, M.D. (2021). Testing Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI Platform. Forests, 12.","DOI":"10.3390\/f12111576"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Buonocore, L., Yates, J., and Valentini, R. (2022). A Proposal for a Forest Digital Twin Framework and Its Perspectives. Forests, 13.","DOI":"10.3390\/f13040498"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8467","DOI":"10.1109\/TIP.2020.3016431","article-title":"An Efficient Fire Detection Method Based on Multiscale Feature Extraction, Implicit Deep Supervision and Channel Attention Mechanism","volume":"29","author":"Li","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1109\/TCSVT.2015.2392531","article-title":"Real-Time Fire Detection for Video-Surveillance Applications Using a Combination of Experts Based on Color, Shape, and Motion","volume":"25","author":"Foggia","year":"2015","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_40","unstructured":"University of Georgia (2024, March 12). ForestryImages Dataset. Available online: https:\/\/www.forestryimages.org\/."},{"key":"ref_41","unstructured":"Cazzolato, M.T., Avalhais, L.P., Chino, D.Y., Ramos, J.S., de Souza, J.A., Rodrigues, J.F., and Traina, A. (2017, January 2\u20135). Fismo: A Compilation of Datasets From Emergency Situations for Fire and Smoke Analysis. Proceedings of the Brazilian symposium on databases-SBBD, Uberlandia, Brazil."},{"key":"ref_42","unstructured":"Flickr Team (2024, March 12). Flickr-FireSmoke and Flickr-Fire Datasets. Available online: https:\/\/www.flickr.com\/."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cazzolato, M.T., Bedo, M.V.N., Costa, A.F., de Souza, J.A., Traina, C., Rodrigues, J.F., and Traina, A.J.M. (2016, January 4\u20138). Unveiling Smoke in Social Images with the SmokeBlock Approach. Proceedings of the 31st Annual ACM Symposium on Applied Computing, Pisa, Italy.","DOI":"10.1145\/2851613.2851634"},{"key":"ref_44","unstructured":"Treneska, S., and Stojkoska, B.R. (2021, January 12\u201314). Wildfire Detection from UAV Collected Images Using Transfer Learning. Proceedings of the 18th International Conference on Informatics and Information Technologies, Xi\u2019an, China."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ghali, R., Akhloufi, M.A., and Mseddi, W.S. (2022). Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation. Sensors, 22.","DOI":"10.3390\/s22051977"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wang, M., Fu, Y., and Ding, Y. (2022). A Forest Fire Recognition Method Using UAV Images Based on Transfer Learning. Forests, 13.","DOI":"10.3390\/f13070975"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s42408-023-00216-0","article-title":"FireXnet: An Explainable AI-based Tailored Deep Learning Model for Wildfire Detection on Resource-constrained Devices","volume":"19","author":"Ahmad","year":"2023","journal-title":"Fire Ecol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"32671","DOI":"10.1109\/ACCESS.2023.3262701","article-title":"A Deep Learning-Based Experiment on Forest Wildfire Detection in Machine Vision Course","volume":"11","author":"Wang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_49","first-page":"101181","article-title":"SegNet: A segmented Deep Learning Based Convolutional Neural Network Approach for Drones Wildfire Detection","volume":"34","author":"Jonnalagadda","year":"2024","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1109\/JSYST.2011.2125230","article-title":"A Comparative Review on Wildfire Simulators","volume":"5","author":"Papadopoulos","year":"2011","journal-title":"IEEE Syst. J."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Li, Y., Hu, J., Wen, Y., Evangelidis, G., Salahi, K., Wang, Y., Tulyakov, S., and Ren, J. (2023, January 2\u20136). Rethinking Vision Transformers for MobileNet Size and Speed. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.01549"},{"key":"ref_52","unstructured":"Tan, M., and Le, Q.V. (2021, January 18\u201324). EfficientNetV2: Smaller Models and Faster Training. Proceedings of the 38th International Conference on Machine Learning, Virtual Event."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., and Doll\u00e1r, P. (2020, January 14\u201319). Designing Network Design Spaces. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, DC, USA.","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"ref_54","unstructured":"Gao, Z., Laurens, V.D.M., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_55","unstructured":"Tan, M., and Le, Q.V. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_56","unstructured":"Saied, A. (2024, March 12). Fire Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/phylake1337\/fire-dataset?select=fire_dataset%2C+06.11.2021."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2196370","DOI":"10.1080\/19475705.2023.2196370","article-title":"Uni-temporal Sentinel-2 Imagery for Wildfire Detection Using Deep Learning Semantic Segmentation Models","volume":"14","author":"Ilyas","year":"2023","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1627\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:38:34Z","timestamp":1760107114000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1627"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,2]]},"references-count":58,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16091627"],"URL":"https:\/\/doi.org\/10.3390\/rs16091627","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,2]]}}}