Infrastructure for Computer Vision Property Assessment

Computer vision property assessment requires documented and findable underwriting guidelines defining how roof condition scores map to inspection requirement flags, which defensible space measurements trigger wildfire risk ratings, and what property features (pools, trampolines) require manual review. State insurance department rate filings mandate that these guidelines exist and are current. The AI must apply consistent logic across aerial and street-level imagery analysis — without findable documentation of the scoring criteria, assessors cannot verify that the AI's premium adjustment recommendations align with filed rate factors.

Property assessment via computer vision requires automated capture of aerial imagery, street-level photos, and applicant-submitted images with associated metadata (capture date, coordinates, resolution, source). This cannot rely on manual upload workflows — satellite imagery must be ingested automatically from providers, historical imagery must be captured for change detection analysis, and applicant photos must be tagged with property address and submission timestamp upon receipt. Without automated capture from imagery workflows, the AI cannot perform temporal change detection (roof aging between renewals) or systematically verify applicant-submitted photos against third-party aerial sources.

Computer vision output requires formal ontology mapping image-detected property features to underwriting risk factors: RoofCondition.Score maps to PremiumAdjustment.Factor and InspectionRequired.Flag, VegetationProximity.Meters maps to WildfireRisk.Rating. Without explicit entity definitions and relationships between visual features and rating variables, the AI generates image analysis outputs that underwriters must manually interpret and translate into pricing decisions — defeating the automation purpose. The formal schema must also link PropertyAddress to ParcelData, enabling the system to join imagery findings with policy records.

Property assessment requires API access to aerial imagery providers, parcel databases, property records, and the underwriting system to write assessment results. Insurance underwriting systems built 10-20 years ago have limited API capability, but third-party imagery providers (satellite, street-level) have APIs available. API access enables the computer vision system to pull property images by address, query parcel data for structural characteristics, and post assessment results to the underwriting workflow without manual export-import cycles that would create processing delays during high-volume renewal periods.

Property assessment criteria must update when climate risk models are revised, when wildfire zone maps are updated, or when state regulatory guidance changes what property features require inspection. Insurance regulatory filings create event-triggered update requirements — when a state updates its wildfire risk territory definitions, the computer vision system's defensible space thresholds and vegetation proximity rules must be recalibrated to reflect the new risk mapping. Imagery baselines also require triggered refresh when significant weather events affect property condition distributions in geographic areas.

Computer vision property assessment requires integration between aerial imagery providers, parcel/property databases, the underwriting system, and the rating engine to translate assessment findings into premium adjustments. Insurance underwriting systems integrate with rating engines via point-to-point connections, and third-party data providers connect through middleware. API-based connections enable the assessment system to retrieve images, join parcel data, post risk findings, and trigger rating recalculation within a single underwriting workflow — from address submission through premium recommendation without manual handoffs.