Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation
The world has seen an increase in the number of wildland fires in recent years due to various factors. Experts warn that the number of wildland fires will continue to increase in the coming years, mainly because of climate change. Numerous safety mechanisms such as remote fire detection systems base...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/7/1821 |
| _version_ | 1827746566311510016 |
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| author | Rafik Ghali Moulay A. Akhloufi |
| author_facet | Rafik Ghali Moulay A. Akhloufi |
| author_sort | Rafik Ghali |
| collection | DOAJ |
| description | The world has seen an increase in the number of wildland fires in recent years due to various factors. Experts warn that the number of wildland fires will continue to increase in the coming years, mainly because of climate change. Numerous safety mechanisms such as remote fire detection systems based on deep learning models and vision transformers have been developed recently, showing promising solutions for these tasks. To the best of our knowledge, there are a limited number of published studies in the literature, which address the implementation of deep learning models for wildland fire classification, detection, and segmentation tasks. As such, in this paper, we present an up-to-date and comprehensive review and analysis of these vision methods and their performances. First, previous works related to wildland fire classification, detection, and segmentation based on deep learning including vision transformers are reviewed. Then, the most popular and public datasets used for these tasks are presented. Finally, this review discusses the challenges present in existing works. Our analysis shows how deep learning approaches outperform traditional machine learning methods and can significantly improve the performance in detecting, segmenting, and classifying wildfires. In addition, we present the main research gaps and future directions for researchers to develop more accurate models in these fields. |
| first_indexed | 2024-03-11T05:26:08Z |
| format | Article |
| id | doaj.art-bce275f01d9544379060cefb9cb77d49 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T05:26:08Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-bce275f01d9544379060cefb9cb77d492023-11-17T17:29:25ZengMDPI AGRemote Sensing2072-42922023-03-01157182110.3390/rs15071821Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and SegmentationRafik Ghali0Moulay A. Akhloufi1Perception, Robotics and Intelligent Machines Research Group (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1A 3E9, CanadaPerception, Robotics and Intelligent Machines Research Group (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1A 3E9, CanadaThe world has seen an increase in the number of wildland fires in recent years due to various factors. Experts warn that the number of wildland fires will continue to increase in the coming years, mainly because of climate change. Numerous safety mechanisms such as remote fire detection systems based on deep learning models and vision transformers have been developed recently, showing promising solutions for these tasks. To the best of our knowledge, there are a limited number of published studies in the literature, which address the implementation of deep learning models for wildland fire classification, detection, and segmentation tasks. As such, in this paper, we present an up-to-date and comprehensive review and analysis of these vision methods and their performances. First, previous works related to wildland fire classification, detection, and segmentation based on deep learning including vision transformers are reviewed. Then, the most popular and public datasets used for these tasks are presented. Finally, this review discusses the challenges present in existing works. Our analysis shows how deep learning approaches outperform traditional machine learning methods and can significantly improve the performance in detecting, segmenting, and classifying wildfires. In addition, we present the main research gaps and future directions for researchers to develop more accurate models in these fields.https://www.mdpi.com/2072-4292/15/7/1821wildland fire detectionwildland fire segmentationwildland fire classificationforest firewildfiredrone |
| spellingShingle | Rafik Ghali Moulay A. Akhloufi Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation Remote Sensing wildland fire detection wildland fire segmentation wildland fire classification forest fire wildfire drone |
| title | Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation |
| title_full | Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation |
| title_fullStr | Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation |
| title_full_unstemmed | Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation |
| title_short | Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation |
| title_sort | deep learning approaches for wildland fires remote sensing classification detection and segmentation |
| topic | wildland fire detection wildland fire segmentation wildland fire classification forest fire wildfire drone |
| url | https://www.mdpi.com/2072-4292/15/7/1821 |
| work_keys_str_mv | AT rafikghali deeplearningapproachesforwildlandfiresremotesensingclassificationdetectionandsegmentation AT moulayaakhloufi deeplearningapproachesforwildlandfiresremotesensingclassificationdetectionandsegmentation |