Road surveys, construction monitoring and DPR support.
Four modules on one platform: road condition and inventory, construction monitoring, DPR preparation, and site monitoring for buildings and bridges. Road condition assessment comes first; the other modules are being introduced in phases.
Road Condition & Asset Intelligence
A road survey platform that analyses mobile phone or dashcam video to map visible road defects and roadside assets, with location references and condition records for review.
Condition assessment
AI detects and classifies visible surface defects, records their severity and location, and scores each road section by chainage.
- Potholes
- Cracks and surface distress
- Rutting and deformation
- Shoulder condition
- Damaged road surface
- Geotagged condition scoring
Repair estimation
The report lists mapped defects with estimated repair quantities by defect type, recommendations by location, indicative costs and a priority-based maintenance plan, for engineering review.
Before and after verification
Pre-repair video is compared with post-repair video to show whether each identified defect appears repaired, the extent of the repair and any defects that remain, with before and after images for each location.
Roadside asset inventory
- Road signs
- Road markings
- Guardrails and crash barriers
- Culverts and drainage
- Streetlights
- Trees and utilities
- Right-of-way condition
- Encroachments
How it works
Works with ordinary mobile phones as well as recommended dashcams.
Construction Progress Monitoring
Repeat drone surveys, planned at 15-day intervals, compare visible site changes with the approved programme. Where survey coverage and accuracy allow, the system estimates progress and flags locations for verification.
What is tracked
- Progress by chainage
- Progress by construction stage
- Earthwork, cutting and filling
- Bridges, culverts and structures
- Time-series comparison
- Photographic evidence
Construction stage recognition
The model classifies visible construction stages and estimates progress where the imagery supports reliable measurement. A typical flexible pavement sequence looks like this, though stages vary with each project's design:
GSB: granular sub-base · WMM: wet mix macadam · DBM: dense bituminous macadam · BC: bituminous concrete
Levels, cut and fill, and deviation
Original ground level, survey data and the current surface are compared to estimate cut and fill quantities and deviation from design levels. Layer thickness needs suitable survey or field measurements alongside the imagery. Where confidence is low or deviation is significant, the system marks the location as Inspection Required.
Quality indicators
Visual and survey data can flag surface defects, uneven layers, unusual material appearance and other visible irregularities that may warrant further checks. Compaction compliance has to be established with the applicable field tests.
AI quality observations are risk indicators. They tell your team where to inspect. They do not replace laboratory or field testing.
Post-construction safety inventory
After construction, the platform maps visible road markings, signs, crash barriers and delineators, and flags apparent gaps against a specified checklist. The report supports inspection; it does not replace a formal road safety audit.
AI-assisted DPR Preparation
Drone and LiDAR survey data is used to generate terrain models, longitudinal profiles, cross-sections and preliminary earthwork quantities for a road Detailed Project Report (DPR). Engineering design, further investigations and statutory approvals remain separate parts of the workflow.
Alignment intelligence
Identify and compare alternative alignments against terrain, settlements, existing roads and other constraints, then generate a preliminary alignment.
Standardised survey workflow
Automated engineering analysis
- Existing ground profile
- Longitudinal profile
- Cross-sections
- Preliminary cut, fill and earthwork quantities
- Indicative drainage requirements
- Likely culvert and bridge locations
DPR preparation workflow
Every stage passes through engineering review and approval. Traffic studies, geotechnical investigation, pavement and structural design, and statutory approvals remain part of the project workflow.
Site Intelligence & Digital Twin
Site cameras and computer vision record visible construction activity and compare it with the project's BIM model. How much can be measured depends on camera coverage, model detail and the validation method used.
Observable site activity
- Material movement
- Labour and equipment activity
- Construction activities
- Work progress and productivity
- Safety observations
- Milestones reached
Digital twin
Site imagery and recorded progress are compared with the project BIM model to show visible differences between planned and observed work. How often it updates, and in how much detail, depends on camera coverage and the model available.
5D BIM progress
The 3D model, schedule, quantities and cost are linked to show planned against observed progress, estimated quantity completed, schedule deviation, delayed activities, work-package status and forecast completion.
Bridge stages
Shared data and mapping across all four modules.
All four modules use the same maps, chainage references and project records. Reusing data across projects depends on its format and on the access terms agreed with each client. Capabilities arrive with the module they belong to, so not every item below is available yet.
Data
AI
Intelligence
Access
Outputs
Book a demonstration.
Tell us about your road network or project. We can walk you through the platform with a sample dataset, or discuss whether your own survey data is suitable.
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