用多源数据提升火场范围识别速度与精度，助力应急响应
Enhancing deep learning performance on burned area delineation from SPOT-6/7 imagery for emergency management
- 结合土地覆盖信息作为辅助任务，增强模型鲁棒性
- 在有限数据下SegFormer与U-Net性能相近，但资源开销更大
- 测试时增强可提效，配合混合精度优化可控推理时间
野火后划定烧毁区域（BAs）对评估损失和推动生态恢复至关重要。现有方法依赖计算机视觉模型处理灾后遥感影像，但在时间紧迫的应急场景中适用性不足。本研究提出一种监督语义分割流程，旨在提升基于SPOT-6/7高分辨率影像的BA提取性能与效率。实验采用Dice分数、交并比和推理时间评估。结果表明：在训练数据有限时，U-Net与SegFormer表现相当；但后者资源消耗更高，限制其应急应用。引入土地覆盖数据作为辅助任务可增强模型鲁棒性，且不增加推理时间。测试时增强（Test-Time Augmentation）能提升性能，但会延长推理时间，可通过混合精度等优化方法缓解。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。