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Construction

Industry Challenges in Construction AI

How rProcess Supports the Construction Ecosystem

We annotate complex site imagery, 3D sensor inputs and geospatial datasets to power AI tools that improve safety, productivity and project planning.

 

Annotation Services for Construction

Image & Video Annotation
Image & Video Annotation
  • Worker, machinery & vehicle detection
  • PPE compliance labeling
  • Hazard identification (edges, pits, heavy equipment zones)
  • Material classification (bricks, concrete, rebar, scaffolding)
  • Progress tracking across time-lapse or CCTV footage
  • AI-identified work stages and material classifications
LiDAR & Sensor Fusion Annotation
LiDAR & Sensor Fusion Annotation
  • Object detection for site equipment & structures
  • Terrain & elevation mapping
  • Accessible and restricted zone labeling
  • Spatial modelling for autonomous construction machinery
RADAR & Geospatial Annotation
RADAR & Geospatial Annotation
  • RADAR signal interpretation for low-visibility conditions
  • Drone & aerial geospatial segmentation
  • Landform, boundary & zone mapping
  • Environmental hazard profiling
  • Large-site monitoring using multi-spectral data

Why Choose rProcess?

Expertise in High‑Variability Construction Environments

We accurately annotate dynamic job sites with constantly changing equipment, materials, and personnel, ensuring AI models perform reliably in real-world construction settings.

Advanced Multi‑Tier QC Ensuring 97%+ Accuracy

Our rigorous QA workflow delivers the precision needed for safety, progress tracking, and risk analytics across complex construction scenes.

High‑Volume Capacity Across Drone, CCTV, LiDAR & Geospatial Data

We efficiently process large, multi-sensor datasets to support site monitoring, mapping, and autonomous equipment development.

Scalable Teams Trained in Construction Workflows

Our specialists understand construction-specific use cases and scale quickly for daily, weekly, or project-based workloads.

Build Safer, Smarter Construction Workflows with Production‑Ready Training Data