TrailSafe LiDAR trail audit

Grade a trail from the terrain, not a stopwatch.

Give it a trail line and TrailSafe samples 1 m LiDAR elevation along it, measures gradient, turn radius and cross-slope at every metre, and grades the result against the NZ Mountain Bike Trail Design & Construction Guidelines — automatically, in about 30 seconds, anywhere in New Zealand with LiDAR coverage.

No GIS experience needed — draw a line or upload a KML.
Live LINZ 1 m LiDAR NZ MTB Guidelines, Recreation Aotearoa Nationwide coverage Out-of-grade section reporting

How it works

Trail line in, graded result out
01

Give it a line

Draw it in the trail editor over aerial imagery, or upload a Trailforks/GPS KML export.

02

Sample the terrain

1 m bare-earth LiDAR elevation along every metre of the line — local tiles in Canterbury/Christchurch, LINZ's national mosaic everywhere else with coverage.

03

Measure and grade

Gradient, turn radius and cross-slope computed at every station, graded against the signposted grade's own thresholds from the guidelines.

04

See exactly what's out

Not just pass/fail — every out-of-grade section listed individually with its length, so you know what needs regrading or realigning.

Built for inspectors, not GIS analysts

No spatial-analysis background required

Load a trail line, click Done, read the result. The interactive map, gradient profile and 3D terrain view are built to be read at a glance — the detail is there when you want to drill in, not forced on you up front.

Works beyond Christchurch

The 1 m LiDAR mosaic covers most of the country now, not just Canterbury — the trail editor has a coverage layer so you can check before you start.

What's next

In development
Planned

AI trail camera integration

Bare-earth LiDAR can't see roots, rocks, jump lips, or loose surface — the things a rider actually feels. The plan is to bring in trailside or rider-mounted camera footage, with AI flagging technical features and hazards LiDAR misses, feeding into the same grading pipeline as a live field-observation layer instead of a manual checklist.

Planned

Automatic trail surface classification

Surface condition (sealed, compacted, loose, natural/mud) currently needs a rider to note it by hand, since it directly affects grade but isn't visible in a bare-earth elevation model. The camera/AI layer above is intended to classify it automatically instead, straight from footage.