How AI Is Transforming Road Asset Inventory at Scale

Indian municipalities manage hundreds of kilometers of roads, usually with almost zero spatial data. Sending out engineers with clipboards is slow, wildly inconsistent, and forgotten the moment it's filed. By sticking a LiDAR scanner on a truck and running the pictures through an AI, we are finally changing what is actually possible on a municipal budget.

The data gap

Most Indian municipalities look after 100 to 500 kilometers of roads, but they have no map of what they actually own. Decisions about where to fix potholes or replace streetlights are driven by citizen complaints, local politics, or whichever engineer happens to remember a bad intersection.

There's no baseline. You can't prove things are getting better or worse. But now, Smart City grants and central government programs are demanding georeferenced asset databases before they release funds. The pressure is on to get this data fast.

The technology

We mount LiDAR and 360-degree cameras on a vehicle and drive the network at normal traffic speeds. This mobile mapping approach lets us cover a 200 km city network in less than a week, capturing the road surface, curbs, drains, and signs in one pass.

While the LiDAR gets the geometry, the cameras get the context. Training an AI on both the shape and the color of an object helps it catch things that either sensor would miss on its own.

We run machine learning models over this data to spot potholes, cracks, broken signs, and clogged drains. Generic international AI models struggle with this, so we train our models specifically on Indian road conditions.

Instead of handing over a hard drive of raw point clouds, we deliver a spatial database with photos and condition scores. Everything is loaded into a web GIS that a municipal engineer can use without needing a six-week software training course.

The process

First, the scanner truck drives the roads, covering 40 to 60 km a day. We capture everything, including what you can't see from a satellite, like the underside of an underpass or a street sign hidden under a tree.

Then, automated processing runs the pavement checks and classifies the street furniture. Our QA team reviews the AI's math, fixes the odd mistakes, and makes sure the GPS traces line up with ground control points. For a 200 km network, the computers chew on it for about four to six days.

Finally, we load the data into a web dashboard and hand over the heat maps. The authority gets a practical report identifying the top 20 worst roads in the city, exactly where they are, and what it’s going to cost to fix them.

The invisible problems

When we run these projects for municipalities, the same thing happens every time: 30 to 40 percent of the broken assets we find were completely unknown to the public works department.

That isn't a failure of the engineers on the ground. It's just what happens when you try to manage a sprawling city network with a notebook and a scooter. The AI approach just turns that invisible backlog into a spreadsheet you can prioritize.

Why this actually matters

Cities everywhere face the same math: the infrastructure breaks down faster than humans can track it.

We already know that fixing a road right before it fails is vastly cheaper than rebuilding it after it falls apart. The barrier was always the staggering cost and time of finding out which road was about to fail.

Mobile LiDAR and AI have finally pushed the cost and speed down far enough that tier-2 and tier-3 Indian cities can afford it. It's no longer just for massive NHAI national highway projects.