[论文解读] ALTIS: Automated Loss Triage and Impact Scoring from Sentinel-1 SAR for Property-Level Flood Damage Assessment

Amogh Vinaykumar, Prem Kamasani|arXiv (Cornell University)|Mar 14, 2026

Flood Risk Assessment and Management被引用 0

一句话总结

ALTIS 是一个五阶段管线，将 Sentinel-1 SAR 数据转化为财产级洪水损失分数并附带置信度估计，在洪峰后24–48小时内提供用于理赔管理的排序分诊清单。它引入保险级洪水分诊（IGFT）以及保险对齐的指标 IRR 和 TES。

ABSTRACT

Floods are among the costliest natural catastrophes globally, yet the property and casualty insurance industry's post-event response remains heavily reliant on manual field inspection: slow, expensive, and geographically constrained. Satellite Synthetic Aperture Radar (SAR) offers cloud-penetrating, all-weather imaging uniquely suited to rapid post-flood assessment, but existing research evaluates SAR flood detection against academic benchmarks such as IoU and F1-score that do not capture insurance-workflow requirements. We present ALTIS: a five-stage pipeline transforming raw Sentinel-1 GRD and SLC imagery into property-level impact scores within 24-48 hours of flood peak. Unlike prior approaches producing pixel-level maps or binary outputs, ALTIS delivers a ranked, confidence-scored triage list consumable by claims platforms, integrating (i) multi-temporal SAR change detection using dual-polarization VV/VH intensity and InSAR coherence, (ii) physics-informed depth estimation fusing flood extent with high-resolution DEMs, (iii) property-level zonal statistics from parcel footprints, (iv) depth-damage calibration against NFIP claims, and (v) confidence-scored triage ranking. We formally define Insurance-Grade Flood Triage (IGFT) and introduce the Inspection Reduction Rate (IRR) and Triage Efficiency Score (TES). Using Hurricane Harvey (2017) across Harris County, Texas, we present preliminary analysis grounded in validated sub-components suggesting ALTIS is designed to achieve an IRR of approximately 0.52 at 90% recall of high-severity claims, potentially eliminating over half of unnecessary dispatches. By blending SAR flood intelligence with the realities of claims management, ALTIS establishes a methodological baseline for translating earth observation research into measurable insurance outcomes.

研究动机与目标

• 正式定义保险级洪水分诊（IGFT），以使用 SAR 图像对被保险财产按预计损失进行排名。
• 开发与保险对齐的评估指标（IRR 和 TES），以评估派遣减少和高严重性索赔召回。
• 在飓风哈维上实现 ALTIS 的端到端应用，为保险公司提供快速、财产级分诊基线。

提出的方法

• 将多时相 SAR 变化检测（VV/VH 强度和 InSAR 相干性）与 HAND 地形约束相结合。
• 通过将洪水范围与高分辨率 DEM 及克里金法深度不确定性融合，利用物理信息化的水线方法估计洪水深度。
• 通过按地带统计，利用地块轮廓与 NFIP 深度-损失曲线，计算财产级别的严重性。
• 生成带有置信度的分诊排序输出，适用于理赔管理平台。
• 在没有像素级监督训练、GPU 需求或实时水动力模型的情况下运行，实现 24–48 小时部署。

Figure 1: Optical vs. SAR imagery over the Addicks sector, Harvey 2017. (a) Pre-event optical: clear conditions, parcel footprints visible. (b) Co-event optical: 100% cloud cover for five consecutive days. (c) Sentinel-2 multispectral attempt: severe cloud contamination. (d) Sentinel-1 VH SAR (30 Au

实验结果

研究问题

• RQ1 如何将 SAR 图像从像素级洪水图转换为与保险工作流对齐的财产级分诊排名？
• RQ2 一个针对 NFIP 索赔进行标定的深度感知财产级打分系统是否能在减少不必要实地检查的同时可靠地优先处理高严重损失？
• RQ3 哪些运营指标最能体现 SAR 基洪水分诊在保险方面的收益（派遣减少和召回）？
• RQ4 在城市洪水环境中，基于振幅、相干性和深度约束的多信号 SAR 融合方法在保险应用中的性能如何？
• RQ5 ALTIS 管线在现实事件（如飓风哈维）中的 24–48 小时时延表现如何？

主要发现

• ALTIS 展示了一个可行的端到端管线，在哈里斯县哈维飓风的高严重性索赔中以约 0.52 的巡检降低率（IRR）实现 90% 的召回。
• 管线通过将深度估计与 NFIP 深度-损失曲线结合，提供财产级别的严重性分数（估算分数损失份额）。
• 提出了一个新颖的分诊效率分数（TES），以联合优化派遣减少、高严重性召回和虚假派遣惩罚。
• 分阶段验证基于经过验证的子组件和本地洪水领域属性，使用公开数据和工具，代码与脚本已发布以供基线比较。
• 该方法的目标是在事件发生后 24–48 小时内完成分诊，利用基于云的 SAR 处理，洪水期间避免对光学数据的依赖（云层遮挡）。

Figure 2: ALTIS five-stage pipeline. Sentinel-1 GRD products enter Stage 1 for preprocessing; the resulting $\sigma^{0}$ rasters and InSAR coherence layers feed Stage 2 change detection; flood extent drives Stage 3 kriging depth estimation; parcel intersection and HAZUS functions produce Stage 4 sev