用卫星雷达数据自动评估洪水损毁，帮保险公司减少一半现场勘查。
ALTIS: Automated Loss Triage and Impact Scoring from Sentinel-1 SAR for Property-Level Flood Damage Assessment
- 五阶段流程：从雷达影像到房屋级损毁评分，支持快速决策。
- 在哈里斯县测试中，可实现90%高损案件召回率，减少52%巡查任务。
- 专为保险理赔设计，输出可信度排序的优先处理清单。
洪水是全球最昂贵的自然灾害之一，但保险业灾后响应仍严重依赖人工现场勘查，效率低、成本高且受地理限制。合成孔径雷达（SAR）具备穿透云层、全天候成像能力，适合快速灾后评估，但现有研究多以IoU和F1-score等学术指标衡量，未能反映保险业务需求。本文提出ALTIS：一个五阶段流程，可在洪水峰值后24-48小时内将哨兵-1 GRD与SLC影像转化为房屋级影响评分。不同于以往生成像素级地图或二值输出的方法，ALTIS输出可直接接入理赔系统的排序式、置信度评分的优先处理列表，整合了（i）双极化VV/VH强度与干涉相干性的多时相变化检测，（ii）融合高分辨率数字高程模型的物理引导水深估计，（iii）基于地块边界的房屋级统计，（iv）基于NFIP理赔数据的水深-损毁校准，以及（v）置信度评分的优先级排序。我们定义了保险级洪灾分类（IGFT），引入巡查减少率（IRR）与分类效率得分（TES）。以2017年哈里斯县飓风哈维为例，初步分析表明，该系统可实现约52%的巡查减少率，同时保持90%高严重性索赔召回率，有望消除超过一半的无效外派任务。通过融合遥感智能与理赔管理现实，ALTIS为地球观测研究向可衡量保险成果转化提供了方法基准。
原文摘要 · Abstract (English)
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.
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