Surface and component anomalies
Isolate corrosion, cracks, spalling, deformation, missing hardware, and other component-level conditions across asset types.
Built for infrastructure inspection robotics
Turn every inspection into model-ready evidence.
Connect RGB, thermal, LiDAR, and telemetry data in one traceable loop—from inspection capture to model-ready dataset releases.
One operating record from inspection mission to versioned dataset release.
MCAP + ROS
LiDAR + point clouds
Multi-camera video
Telemetry + trajectories
One operating loop
The inspection mission is only the beginning.
Replace fragmented handoffs with a traceable path from synchronized inspection capture to the next model-ready dataset release.
- 01
Ingest synchronized inspection missions
Bring MCAP, thermal and RGB video, point clouds, GNSS, IMU, and asset events together without stripping away time or source context.
- 02
Curate the evidence that matters
Search and slice by asset, component, anomaly, weather, viewpoint, and sensor condition to assemble the scenarios the next training cycle needs.
- 03
Build defensible ground truth
Combine model assistance with human 2D, 3D, and temporal review, then apply the quality gates each release requires.
- 04
Publish a versioned release
Freeze the manifest contents and source origins, attach declared rights metadata, then publish an immutable revision that the loader resolves for training and evaluation.
Scenario intelligence
Find the evidence that changes model behavior.
Turn long inspection runs into focused training slices while preserving the sensor, asset, and operating context around every event.
Geometry, clearance, and deformation
Preserve aligned point clouds, camera views, poses, calibration, and measurements for structural-change and clearance scenarios.
Occlusion, glare, and weather
Curate the viewpoints and environmental conditions where inspection models lose confidence.
Durable dataset identity
Ship a release your inspection stack can reproduce.
Treat every training set as a governed artifact with a stable address—not a folder passed between teams.
- Friendly dataset reference plus an immutable canonical revision
- Manifest digest, object count, total size, and source origins
- Declared rights document and access terms attached to the revision
- Published state and revision identity returned through the loader
Dataset release manifest
Versioned- Dataset
- infrastructure/bridge-inspection
- Release
- v9
- Origin records
- primary / avala · calibration / avala
- Declared rights
- Attached
- Revision state
- Published
infrastructure/bridge-inspection@v9Built for infrastructure inspection autonomy
One data system across the infrastructure inspection program.
Keep bridge, rail, road, tunnel, and site inspection work on the same dataset and quality foundation.
Bridges and structures
Build temporal and multi-view datasets for decks, piers, cables, joints, façades, and other critical components.
Rail and transit
Align track, overhead line, platform, tunnel, and right-of-way evidence across repeated inspection runs.
Roads and tunnels
Curate pavement, barrier, signage, clearance, and tunnel-surface conditions with location and sensor context intact.
Construction and industrial sites
Review drone, rover, vehicle, and fixed-sensor data for progress, component conditions, and difficult-to-reach assets.
Start with one model bottleneck
Map the inspection-data release your program needs next.
Bring your sensor stack, current workflow, and target behavior. Leave with a scoped path from inspection missions to a versioned dataset.