卫星禁运下的战争损失统计：受影响基础设施的零样本估计
Counting the Cost of War Under Satellite Embargo: Zero-Shot Estimation of Impacted Infrastructure
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中文总结 AI 辅助
针对冲突区人道主义救援中受影响建筑估计受卫星数据禁运阻碍的问题，提出基于打击前地图的零样本几何投影方法，引入两项技术创新，在2026年中东冲突数据上验证了深度增强大视觉语言模型的优势，确立了混合零样本危机映射范式。
中文摘要 AI 辅助
冲突区人道主义救援亟需快速估计受影响建筑，但这一需求常遭打击后卫星数据禁运和影像中断阻碍。我们通过将受影响建筑映射重构为对打击前存档地图的零样本几何投影任务，绕过这一操作瓶颈。利用LiveUAMap和ArcGIS的坐标与事件文本，大语言模型提取武器载荷（W），通过Hopkinson-Cranz缩放公式（R_base = Z * W^(1/3)）投射动能爆炸范围。为在无打击后影像的情况下统计这些区域内的暴露建筑，我们引入两项技术创新：自适应视场以消除2D分割（SAMGeo）中的分辨率（缩放）偏差，以及2.5D伪高度深度图结合分割掩码，帮助大视觉语言模型（LVLMs）分辨重叠、密集的屋顶。在2026年中东冲突数据上评估，深度增强的LVLMs在拥挤城市中心的表现远超传统分割方法。这确立了一种强大的混合零样本危机映射范式：针对稀疏农村区域的超快速2D分割，以及针对密集城市环境的深度增强LVLMs。
英文摘要
Rapid estimation of impacted structures - critical for conflict-zone humanitarian response - is frequently hindered by post-strike satellite data embargoes and imagery blackouts. We bypass this operational bottleneck by reframing impacted building mapping as a zero-shot geometric projection task on archival, pre-strike maps. Using coordinate and incident text from LiveUAMap and ArcGIS, Large Language Models extract weapon payloads (W) to project kinetic blast perimeters via Hopkinson-Cranz scaling (R_base = Z * W^(1/3)). To count exposed structures within these zones without post-strike imagery, we introduce two technical innovations: Adaptive Field-of-View to eliminate resolution (zoom) bias in 2D segmentation (SAMGeo), and 2.5D pseudo-height depth maps combined with segmentation masks to help Large Vision-Language Models (LVLMs) resolve overlapping, dense rooftops. Evaluated on 2026 Middle East conflict data, depth-augmented LVLMs dramatically outperform traditional segmentation in congested urban centers. This establishes a powerful hybrid paradigm for zero-shot crisis mapping: ultra-fast 2D segmentation for sparse rural zones, and depth-augmented LVLMs for dense urban environments.
发表机构
- Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
机构由 AI 辅助整理，请以论文原文为准。
