Computer Vision

T2D2

AI-powered building and asset inspection at scale

An enterprise cloud inspection platform that processes structural imagery, detects facade damage, maps defects spatially, and tracks deterioration across historical timelines.

View T2D2

70%

Faster exception & audit cycles

Engagement-reported

80+

Damage types classified

Engagement-reported

3.5TB+

Forensic image dataset

Engagement-reported

100%

Visual coverage via drone & scans

Qualitative outcome

Numeric figures are engagement-reported from published project work. Baselines and measurement windows are shared with qualified buyers under NDA.

Business challenge

Structural inspection teams rely on manual facade surveys—ropes, scaffolding, or ground-level audits—leaving asset owners exposed to undetected risks, safety hazards, regulatory rejections, and slow turnaround. Massive photo sets from drones and mobile cameras lack centralized defect tracking; human fatigue drives missed surface defects across thousands of images.

Why AI

Manual photo auditing does not scale across forensic image volumes. Computer vision with human engineer review is the right approach when defects must be detected consistently, geotagged, and tracked over time for regulatory reporting.

Business outcomes

Routine manual auditing timelines drop by up to 70%, freeing structural experts for complex exceptions. Early micro-crack detection reduces degradation risk; every defect retains coordinates, timestamps, and image lineage for regulators.

Delivery approach

How the solution was implemented.

Field teams capture imagery; models process elevations for cracks, spalling, and moisture anomalies. Engineers confirm or adjust detections on maps and 3D twins before reports export for maintenance action.

Solution architecture

AWS S3 and cloud pipelines for high-volume image upload
PyTorch models trained on forensic imagery for 80+ defect classes
Angular SPA for responsive defect overlay rendering
Leaflet and Cesium 3D for spatial mapping and digital twins
Node.js API with MongoDB metadata indexing
Integrated assistant for metadata queries and asset search

AI capabilities delivered

AI damage detection with precise bounding boxes
Multi-class defect recognition across material types
Deterioration and trend monitoring over historical timelines
Human audit loop on mapped anomalies
Audit-ready export with spatial citations

Outcome detail

Engineers reclaim analysis time from routine image review
Early defect detection limits structural degradation and regulatory exposure
Audit-ready asset lineage with spatial coordinates and historical trends

Technical highlights

What made the delivery work.

80+ defect types across concrete, brick, stone, and composites

Interactive 3D and map-based defect localization

Historical timeline tracking for deterioration patterns

Forensic dataset scale (3.5TB+) supporting model accuracy

Enterprise technologies used

Python / PyTorch (CV)Angular & Node.jsLeaflet & Cesium 3DAWS Cloud & S3MongoDBCustom CMS/CRM

Lessons learned

CV inspection succeeds when engineers stay in the audit loop—automation handles volume, humans own sign-off.
Spatial lineage is as important as detection accuracy for regulated asset reporting.
Centralized defect tracking beats scattered photo archives.

Discuss a similar AI initiative.

Thirty minutes with an architect to pressure-test fit, constraints, and a practical first slice, NDA available for qualified opportunities.