发表机构
Rice University; Ken Kennedy Institute（莱斯大学; 肯·肯尼迪研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出 DebrisHeightNet 网络，利用飓风后单次航拍 RGB 图像，结合 CW-LMF 等技术实现残骸体积快速估算，精度优于传统参数化方法，部署便捷。
AI 中文摘要
飓风残骸清理的规划、合同签订及联邦报销均以体积估算为依据，但实际操作仍依赖参数化预测（据记录高估率达41%-90%），或仅在拖运开始后才统计卡车装载量。本文提出 DebrisHeightNet，这是一种以分割为条件的单目残骸高度网络，可从飓风登陆后数日内常规开展的单次航拍 RGB 图像中估算空间明确的残骸体积。我们仅在两个冻结的视觉基础模型之上训练一个轻量级（1.08 M 参数）的头部，该头部以 Depth Anything V2 为骨干，根据前期工作中 CLIPSeg-debris 的残骸分割结果来回归高度。由于不存在飓风后残骸高度的真实值，我们通过置信度加权激光雷达-单目融合（CW-LMF）合成训练目标，旨在抑制非残骸激光雷达回波。该融合目标是构造的监督信号而非真实值，因此我们通过外部参考对其进行验证，而非声称其为真实值。基于各区域低密度残骸占比的区域级幂律校准，将模型体积转换为具有量化不确定性的已报告拖运残骸估算值。在覆盖五次飓风、三个州的十个区域中，未校准模型与训练区域的独立无人机（UAV）调查的 Spearman ρ 为 0.87，且在 Hazus 和 FEMA 混合参数化预测高估 2.7-4.8 倍的情况下，模型估算值与已报告记录的偏差在 30%以内。部署无需激光雷达、地面访问或二次飞行，因此只要开展单次事件后航拍，该方法即可生成空间明确的体积估算值。
英文摘要
Hurricane debris removal is planned, contracted, and federally reimbursed on the basis of volume estimates, yet operational practice still relies on parametric forecasts with 41-90% documented over-estimation or on truck-load tallies that arrive only after hauling begins. We present DebrisHeightNet, a segmentation-conditioned monocular debris-height network that estimates spatially explicit debris volume from a single pass of post-event aerial RGB imagery, the kind of survey routinely flown within days of a hurricane landfall. We train only a lightweight 1.08 M-parameter head on top of two frozen vision foundation models. This head regresses height from a Depth Anything V2 backbone, conditioned on the debris segmentation of CLIPSeg-debris from our prior work. Because no post-hurricane debris-height ground truth exists, we synthesize the training target by confidence-weighted LiDAR-monocular fusion (CW-LMF), designed to suppress non-debris LiDAR returns. This fused target is a constructed supervision signal rather than ground truth, so we corroborate it against external references rather than claiming it as truth. A region-level power-law calibration, driven by each region's low-density debris fraction, converts model volume into an estimate of the reported hauled debris with quantified uncertainty. Across ten regions spanning five hurricanes and three states, the uncalibrated model agrees with an independent uncrewed-aerial-vehicle (UAV) survey of the training region at Spearman $ρ= 0.87$ and lands within 30% of the reported record where the Hazus and FEMA-hybrid parametric forecasts over-predict it by 2.7-4.8$\times$. Deployment requires no LiDAR, no ground access, and no second flight, so the method can produce spatially explicit volume estimates wherever single-pass post-event imagery is flown.
