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ALTIS: Automated Loss Triage and Impact Scoring from Sentinel-1 SAR for Property-Level Flood Damage Assessment

Amogh Vinaykumar, Prem Kamasani

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.

ALTIS: Automated Loss Triage and Impact Scoring from Sentinel-1 SAR for Property-Level Flood Damage Assessment

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.
Paper Structure (64 sections, 23 equations, 9 figures, 7 tables)

This paper contains 64 sections, 23 equations, 9 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. SAR-Based Flood Detection
  4. The Translational Gap in Insurance Applications
  5. SAR Flood Depth Estimation
  6. Flood Damage Modelling for Insurance
  7. Data Sources and SAR Motivation
  8. Problem Formulation
  9. Task Definition
  10. Severity Score and Estimated Fractional Loss
  11. Evaluation Metrics
  12. ALTIS Pipeline Architecture
  13. Stage 1: SAR Data Acquisition and Preprocessing
  14. Product Selection.
  15. Preprocessing Chain.
  16. ...and 49 more sections

Figures (9)

  • 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 Aug 2017): unambiguous flood signal penetrating cloud cover, parcels coloured by ALTIS severity score. The cloud occlusion in (b) motivates SAR as the only operationally viable modality for 24--48 hour post-event triage.
  • 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 severity scores; Stage 5 ranks and tiers all FNOL properties for adjuster dispatch.
  • Figure 3: Sentinel-1 VH false-colour SAR composite over Harris County, Texas, Hurricane Harvey, 30 August 2017. Red areas indicate backscatter decrease consistent with inundation; cyan/grey areas are non-flooded; dark navy is permanent water. Key geographic features and the Addicks/Barker Reservoir overflow zones are annotated. This composite is the primary Stage 1 output and the visual basis for Stage 2 BCR computation.
  • Figure 4: Feature-space separability of ALTIS signal channels, Harvey 2017. (a) VH vs. VV BCR for flooded (blue), non-flooded (grey), and HAND-excluded (red) pixels. (b) Pre-event vs. co-event coherence. (c) HAND value vs. VH BCR showing the terrain gate at 10 m. (d) ALTIS severity score vs. NFIP claim payment. Each channel provides distinct discriminative information; their combination motivates the Bayesian fusion in Equation \ref{['eq:bayes']}.
  • Figure 5: Stage 2 BCR signal decomposition, Harris County TX, 30 August 2017. (a--b) Pre- and co-event VH false-colour composites with Addicks Reservoir annotation box. (c--d) BCR map and BCR filtered by HAND mask (red hatching shows HAND-excluded pixels). (e--f) Addicks sector zoom: aerial optical context and BCR+HAND zoom confirming HAND elimination of reservoir dam artefacts. (g--h) Binary flood masks from BCR+HAND and full ALTIS BCR+CCI+HAND; the coherence integration recovers flooded urban blocks missed by amplitude-only detection.
  • ...and 4 more figures

Theorems & Definitions (1)

  • Definition 1: Insurance-Grade Flood Triage