How AI Roof Measurements Achieve 97% Accuracy

Traditional roof measurement methods require a crew to physically climb onto a roof, take measurements, and manually calculate areas. This process is time-consuming, dangerous, and prone to human error.

The Google Solar API Foundation

RoofScanTech starts with Google's Solar API, which provides high-resolution aerial imagery and pre-computed building footprints. This data is sourced from the same satellite and aerial photography used by Google Maps, giving us centimeter-level precision for building boundaries.

AI Vision Enhancement

Our AI vision pipeline analyzes satellite imagery, street view photos, and (optionally) drone photography to detect roof features that affect measurements: valleys, hips, dormers, chimneys, and skylights. Each feature is identified and its impact on total roof area is calculated.

Multi-Source Data Fusion

The real magic happens when we fuse data from multiple sources. By combining Solar API measurements, AI-detected features, and (when available) drone photogrammetry, we achieve accuracy within 2-3% of manual measurement. For most quoting purposes, this eliminates the need for a site visit entirely.

Continuous Calibration

Our measurement engine continuously improves. When contractors provide actual roof dimensions after installation, we use that feedback to calibrate our models for specific geographic regions and roof types, further improving accuracy over time.