WEGtrax fuses satellite radar and optical imagery into a living risk map of your road network — flagging failures weeks before they become visible.
Road agencies only mobilize when damage is severe — potholes, closures, emergencies. WEGtrax detects the precursors weeks earlier.
Ground vehicles are slow, expensive, and leave most roads unmeasured between campaigns. Satellite covers 100% with every pass.
Sinkholes, moisture infiltration, mm-scale deformation — invisible to any ground inspection, detectable with InSAR from orbit.
SAR · InSAR · Optical AI fused into a single map. Roughness, deformation, distress, flood, lane marking — all in one place.
A real map of the Netherlands with live risk data overlaid. Click segments or markers to inspect IRI scores, subsidence rates, and maintenance forecasts.
Every module outputs georeferenced, API-ready data layers.
AI-powered International Roughness Index using SAR pixel intensity — at national scale, continuously.
SAR · CNN · IRIMillimeter-level vertical movement detected with InSAR — sinkholes flagged before surface failure.
InSAR · mm-precisionHigh-res optical AI scores lane visibility and flags non-compliant segments for repainting.
Optical · Semantic AIForecast remaining useful life, future distress levels, and optimal resurfacing windows months ahead.
ML · Time-seriesCracking density, rutting, and pothole clusters — severity-graded via SAR + optical fusion.
SAR + OpticalFlooded segments, sub-pavement water, and moisture-weakening assessment for post-event triage.
SAR backscatterLandslides, debris, blocked highways — detected and geo-flagged automatically.
Change detectionActive construction detection, monthly progress, and delay-pattern identification.
Multi-temporalAll 8 capabilities run on existing satellites — no sensors, no vehicles, no field teams.
Talk to usSAR, InSAR, and high-res optical imagery ingested alongside crowdsourced signals for a cross-validated picture.
Inputs co-registered, normalized, filtered through automated quality control — noise reduced, signals preserved.
Deep learning models recognize roughness, deformation, and hazard patterns — trained on extensive ground truth.
Georeferenced risk maps, maintenance-priority queues, and API exports feeding directly into planning tools.
Monitor entire road networks — rural B-roads to motorways — with consistent, continuous quality.
Eliminating manual survey campaigns significantly cuts operational carbon footprint.
Predictive prioritization reduces maintenance costs by up to 40% vs reactive repair.
Your risk picture updates with every satellite pass — not once a year per contract cycle.
We run a scoped pilot on your road data so you can validate outputs before full deployment. No hardware — just results.
Road authority, municipality, or partner — we'd like to show you what satellite AI can do for your network.
For demos, pilots, partnerships, or press.
