无卫星影像时，用预存地图和大模型估算战争损毁建筑。
Counting the Cost of War Under Satellite Embargo: Zero-Shot Estimation of Impacted Infrastructure
- 用战前地图和武器数据推算爆炸范围，实现零样本估算。
- 在密集城区中，深度增强模型比传统方法多识别37%损毁建筑。
- 适合人道救援、军事评估等需快速响应的危机场景。
冲突区灾后建筑损毁快速评估对人道援助至关重要，但常因战后卫星数据禁令与图像中断而受阻。本文将损毁建筑定位重构为基于历史地图的零样本几何投影任务。利用LiveUAMap与ArcGIS中的坐标和事件文本，大语言模型提取武器载荷（W），通过Hopkinson-Cranz标度公式计算基础爆炸半径（R_base = Z * W^(1/3)）。为在无战后影像条件下统计暴露建筑，提出两项创新：自适应视场（Adaptive Field-of-View）消除2D分割中的分辨率偏倚（采用SAMGeo），以及2.5D伪高程深度图结合分割掩码，辅助大视觉语言模型（LVLMs）解析重叠密集屋顶。在2026年中东冲突数据上验证，深度增强的LVLM在城市中心显著优于传统分割方法，准确率提升37%。该研究建立了一种混合范式：稀疏农村区域采用超快2D分割，密集城市则使用深度增强的LVLM，实现高效零样本危机制图。
原文摘要 · Abstract (English)
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.
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