Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation
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 wi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/15/7046 |
| _version_ | 1797525787461550080 |
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| author | Jorge Francisco Ciprián-Sánchez Gilberto Ochoa-Ruiz Lucile Rossi Frédéric Morandini |
| author_facet | Jorge Francisco Ciprián-Sánchez Gilberto Ochoa-Ruiz Lucile Rossi Frédéric Morandini |
| author_sort | Jorge Francisco Ciprián-Sánchez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:18:55Z |
| format | Article |
| id | doaj.art-8321967735c54edebf53fd41415b83b4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:18:55Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8321967735c54edebf53fd41415b83b42023-11-22T05:23:38ZengMDPI AGApplied Sciences2076-34172021-07-011115704610.3390/app11157046Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire SegmentationJorge Francisco Ciprián-Sánchez0Gilberto Ochoa-Ruiz1Lucile Rossi2Frédéric Morandini3School of Engineering and Sciences, Tecnologico de Monterrey, Av. Lago de Guadalupe KM 3.5, Margarita Maza de Juárez, Ciudad López Mateos 52926, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501, Monterrey 64849, MexicoLaboratoire Sciences Pour l’Environnement, Campus Grimaldi—BP 52, Università di Corsica, 20250 Corte, FranceLaboratoire Sciences Pour l’Environnement, Campus Grimaldi—BP 52, Università di Corsica, 20250 Corte, FranceWildfires 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.https://www.mdpi.com/2076-3417/11/15/7046wildfiresdeep learningsegmentationloss functionarchitecture |
| spellingShingle | Jorge Francisco Ciprián-Sánchez Gilberto Ochoa-Ruiz Lucile Rossi Frédéric Morandini Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation Applied Sciences wildfires deep learning segmentation loss function architecture |
| title | Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation |
| title_full | Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation |
| title_fullStr | Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation |
| title_full_unstemmed | Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation |
| title_short | Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation |
| title_sort | assessing the impact of the loss function architecture and image type for deep learning based wildfire segmentation |
| topic | wildfires deep learning segmentation loss function architecture |
| url | https://www.mdpi.com/2076-3417/11/15/7046 |
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