Enhancing deep learning performance on burned area delineation from SPOT-6/7 imagery for emergency management
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917144266014720 |
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| author | Rodriguez, Maria Pham, Minh-Tan Sudmanns, Martin Poterek, Quentin Narvaez, Oscar |
| author_facet | Rodriguez, Maria Pham, Minh-Tan Sudmanns, Martin Poterek, Quentin Narvaez, Oscar |
| contents | After a wildfire, delineating burned areas (BAs) is crucial for quantifying damages and supporting ecosystem recovery. Current BA mapping approaches rely on computer vision models trained on post-event remote sensing imagery, but often overlook their applicability to time-constrained emergency management scenarios. This study introduces a supervised semantic segmentation workflow aimed at boosting both the performance and efficiency of BA delineation. It targets SPOT-6/7 imagery due to its very high resolution and on-demand availability. Experiments are evaluated based on Dice score, Intersection over Union, and inference time. The results show that U-Net and SegFormer models perform similarly with limited training data. However, SegFormer requires more resources, challenging its practical use in emergencies. Incorporating land cover data as an auxiliary task enhances model robustness without increasing inference time. Lastly, Test-Time Augmentation improves BA delineation performance but raises inference time, which can be mitigated with optimization methods like Mixed Precision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12056 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing deep learning performance on burned area delineation from SPOT-6/7 imagery for emergency management Rodriguez, Maria Pham, Minh-Tan Sudmanns, Martin Poterek, Quentin Narvaez, Oscar Computer Vision and Pattern Recognition After a wildfire, delineating burned areas (BAs) is crucial for quantifying damages and supporting ecosystem recovery. Current BA mapping approaches rely on computer vision models trained on post-event remote sensing imagery, but often overlook their applicability to time-constrained emergency management scenarios. This study introduces a supervised semantic segmentation workflow aimed at boosting both the performance and efficiency of BA delineation. It targets SPOT-6/7 imagery due to its very high resolution and on-demand availability. Experiments are evaluated based on Dice score, Intersection over Union, and inference time. The results show that U-Net and SegFormer models perform similarly with limited training data. However, SegFormer requires more resources, challenging its practical use in emergencies. Incorporating land cover data as an auxiliary task enhances model robustness without increasing inference time. Lastly, Test-Time Augmentation improves BA delineation performance but raises inference time, which can be mitigated with optimization methods like Mixed Precision. |
| title | Enhancing deep learning performance on burned area delineation from SPOT-6/7 imagery for emergency management |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.12056 |