Property-Level Flood Risk Assessment Using AI-Enabled Street-View Lowest Floor Elevation Extraction and ML Imputation Across Texas

2026-04-01

AI Summary

The absence of building-scale first-floor elevation data hinders fine-grained assessment of regional flood risk. This study presents the first end-to-end regional workflow that automatically extracts building first-floor elevations from Google Street View imagery using the Elev-Vision framework and imputes missing values by integrating random forest and gradient boosting models trained on 16 topographic, hydrologic, and geographic features. Applied across 18 regions in Texas, the approach successfully retrieved elevation data for 49.0% of 12,241 residential structures, with imputation models in 13 regions achieving R² values ranging from 0.159 to 0.974. This significantly enhances the capacity to assess building-level flood exposure and interior inundation losses in areas lacking elevation certificates.

Abstract

This paper argues that AI-enabled analysis of street-view imagery, complemented by performance-gated machine-learning imputation, provides a viable pathway for generating building-specific elevation data at regional scale for flood risk assessment. We develop and apply a three-stage pipeline across 18 areas of interest (AOIs) in Texas that (1) extracts LFE and the height difference between street grade and the lowest floor (HDSL) from Google Street View imagery using the Elev-Vision framework, (2) imputes missing HDSL values with Random Forest and Gradient Boosting models trained on 16 terrain, hydrologic, geographic, and flood-exposure features, and (3) integrates the resulting elevation dataset with Fathom 1-in-100 year inundation surfaces and USACE depth-damage functions to estimate property-specific interior flood depth and expected loss. Across 12,241 residential structures, street-view imagery was available for 73.4% of parcels and direct LFE/HDSL extraction was successful for 49.0% (5,992 structures). Imputation was retained for 13 AOIs where cross-validated performance was defensible, with selected models achieving R suqre values from 0.159 to 0.974; five AOIs were explicitly excluded from prediction because performance was insufficient. The results show that street-view-based elevation mapping is not universally available for every property, but it is sufficiently scalable to materially improve regional flood-risk characterization by moving beyond hazard exposure to structure-level estimates of interior inundation and expected damage. Scientifically, the study advances LFE estimation from a pilot-scale proof of concept to a regional, end-to-end workflow. Practically, it offers a replicable framework for jurisdictions that lack comprehensive Elevation Certificates but need parcel-level information to support mitigation, planning, and flood-risk management.

Problem

• flood risk assessment
• lowest floor elevation
• property-level
• street-view imagery
• elevation data

Innovation

• street-view imagery
• lowest floor elevation
• machine learning imputation
• flood risk assessment
• property-level modeling