Abstract
Aerial imagery is critical for large-scale post-disaster damage assessment. Automated interpretation remains challenging due to clutter, visual variability, and strong cross-event domain shift, while supervised approaches still rely on costly, task-specific annotations with limited coverage across disaster types and regions. Recent open-vocabulary and foundation vision models offer an appealing alternative, by reducing dependence on fixed label sets and extensive task-specific annotations. Instead, they leverage large-scale pretraining and vision-language representations. These properties are particularly relevant for post-disaster domains, where visual concepts are ambiguous and data availability is constrained. In this work, we present a comparative evaluation of supervised learning and open-vocabulary vision models for post-disaster scene understanding, focusing on semantic segmentation and object detection across multiple datasets, including FloodNet+, RescueNet, DFire, and LADD. We examine performance trends, failure modes, and practical trade-offs between different learning paradigms, providing insight into their applicability for real-world disaster response. The most notable remark across all evaluated benchmarks is that supervised training remains the most reliable approach (i.e., when the label space is fixed and annotations are available), especially for small objects and fine boundary delineation in cluttered scenes.
Abstract (translated)
航空影像对于大规模灾后损害评估至关重要。由于杂乱的环境、视觉变化性和强烈的事件间域偏移，自动解读仍面临挑战，而监督方法仍然依赖于成本高昂的任务特定标注，并且这些标注在灾难类型和地区方面的覆盖率有限。最近开放词汇和基础视觉模型提供了一种有吸引力的选择，它们减少了对固定标签集和大量任务特定注释的依赖，而是利用大规模预训练和视觉语言表示。对于灾后领域来说，这种特性尤为重要，因为这里的视觉概念模糊且数据可用性受限。 在这项工作中，我们展示了监督学习与开放词汇视觉模型在灾后场景理解方面的比较评估，重点在于多个数据集上的语义分割和目标检测，包括FloodNet+、RescueNet、DFire和LADD。我们研究了性能趋势、失败模式以及不同学习范式之间的实际权衡，提供了对它们在现实世界灾害响应中的适用性的见解。 所有评估基准中最值得注意的一点是，在标签空间固定且有注释可用的情况下，监督训练仍然是最可靠的方法（特别是对于小对象和杂乱场景中精细边界划分）。
URL
https://arxiv.org/abs/2603.01324
