The increasing frequency and intensity of natural disasters demand more sophisticated approaches for rapid and precise damage assessment. To tackle this issue, researchers have developed various methods on disaster benchmark datasets from satellite imagery to aid in detecting disaster damage. However, the diverse nature of geographical landscapes and disasters makes it challenging to apply existing methods to regions unseen during training. We present DAVI (Disaster Assessment with VIsion foundation model), which overcomes domain disparities and detects structural damage (e.g., building) without requiring ground-truth labels of the target region. DAVI integrates task-specific knowledge from a model trained on source regions with an image segmentation foundation model to generate pseudo labels of possible damage in the target region. It then employs a two-stage refinement process, targeting both the pixel and overall image, to more accurately pinpoint changes in disaster-struck areas based on before-and-after images. Comprehensive evaluations demonstrate that DAVI achieves exceptional performance across diverse terrains (e.g., USA and Mexico) and disaster types (e.g., wildfires, hurricanes, and earthquakes). This confirms its robustness in assessing disaster impact without dependence on ground-truth labels.

翻译：自然灾害日益频繁和加剧，要求采用更先进的方法进行快速精确的损害评估。为应对此问题，研究人员已基于卫星影像的灾害基准数据集开发了多种方法，以协助检测灾害损害。然而，地理景观和灾害类型的多样性使得现有方法难以直接应用于训练阶段未见的区域。本文提出DAVI（基于视觉基础模型的灾害评估），该方法能够克服领域差异，并在无需目标区域真实标注的情况下检测结构损害（如建筑物）。DAVI通过整合源区域训练模型获得的任务特定知识与图像分割基础模型，生成目标区域可能损害区域的伪标签。随后采用针对像素和整体图像的两阶段优化流程，基于灾前灾后影像更精确地定位受灾区域的变化。综合评估表明，DAVI在不同地形（如美国和墨西哥）与灾害类型（如野火、飓风和地震）中均表现出卓越性能，这证实了其在无需依赖真实标注的情况下评估灾害影响的鲁棒性。