Abstract
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.
Abstract (translated)
在野火之后，界定烧毁区域（Burned Areas，BAs）对于量化损失和支持生态系统恢复至关重要。当前的BA制图方法依赖于基于事后遥感图像训练的计算机视觉模型，但通常忽略了这些方法在时间紧迫的应急管理工作中的适用性。本研究引入了一种监督语义分割工作流程，旨在提升BA界定的性能和效率。该工作流程针对SPOT-6/7影像，因其非常高的分辨率及按需获取的特点而被选定为目标数据源。实验评估基于Dice系数、交并比（Intersection over Union）以及推理时间进行。 研究结果显示，在训练数据有限的情况下，U-Net和SegFormer模型表现类似。然而，SegFormer需要更多资源，这对其在紧急情况下的实际应用构成了挑战。将土地覆盖数据作为辅助任务纳入模型可以增强模型的鲁棒性而不会增加推理时间。最后，测试时的数据增强（Test-Time Augmentation）能提升BA界定性能，但会增加推理时间，这一问题可通过混合精度等优化方法来缓解。
URL
https://arxiv.org/abs/2512.12056
