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.

DAVI 是一种基于图像分割和任务特定知识的灾害评估方法，能够在无需目标地区地面真值标签的情况下，准确检测结构性损害，并在不同地理景观和灾害类型的情况下展现出优异性能。