Augmented Reality for Utilities: Seeing Underground Assets in the Field

What is augmented reality for utilities?

Augmented reality for utilities shows network assets from a GIS on top of a live camera view. So a crew member can point a tablet at a road and see the water main, the cable duct and the gas service below it.

In simple terms, the device knows where it is and which way it faces. Then it draws each asset at its mapped place and depth. As a result, a flat map turns into a 3D view that matches what the crew sees on site. Field staff no longer need to read a plan, pace out offsets and guess where a line runs.

However, AR is a view of your records, not a scan of the ground. It cannot see a pipe that your GIS does not hold. Also, it cannot fix a cable that your records place two metres away from its real route. That is why this guide spends as much time on data and accuracy as on the device. For a wider view of network GIS, see our guide to GIS for electric, water, gas and telecom utilities.

How does GIS data feed an AR view in the field?

GIS data feeds AR through a chain of steps. First, the system takes network features from the GIS. Next, it adds depth and size, and then it streams them to a mobile app that places them in 3D.

But most utility GIS data is 2D. For example, a water main may exist only as a line with a diameter field. So the AR app needs a rule to turn that line into a pipe at the right depth. Sometimes the depth comes from survey records. In other cases, teams use a default cover depth and flag it as an estimate. Then the app also needs ground height, since depth is measured from the surface.

1. Select the layers that matter for the task, such as mains, services, ducts and valves.
2. Add depth from survey, as-built plans or a default value, and tag the source of each value.
3. Build 3D pipes and cables from line width, diameter and depth fields.
4. Publish the layers as a service that the mobile app can read, online or offline.
5. Place the assets on the camera view using the device position, heading and ground height.

Esri offers a working pattern for this. Its ArcGIS Maps SDK sample for hidden infrastructure lets users draw pipes, set them above or below ground and view them in AR. In addition, the sample places pipes against Esri’s world elevation service. It also notes that buried pipes can seem to float, because of parallax. So crews need short training on how to read the view.

Data from mobile mapping can also help. For instance, LiDAR can capture covers, poles and kerbs with precise heights. Our article on LiDAR feature extraction for utilities explains how teams turn point clouds into GIS features that AR can use.

How accurate is augmented reality for underground assets?

AR accuracy depends on two things: where the device thinks it is, and where the GIS says the asset is. If either one is off, the pipe on screen will sit in the wrong place.

First, device position is the limit. According to GPS.gov, GPS-enabled smartphones are typically accurate to within 4.9 metres under open sky. Also, accuracy gets worse near buildings, bridges and trees. That is a common setting for a utility trench in a city. So a phone on its own is fine for a quick look, but not for close work near a live cable.

However, high-accuracy GNSS closes much of that gap. GPS.gov notes that dual-frequency receivers and correction systems can give real-time positions within a few centimetres. For example, Trimble lists centimetre RTK accuracy for its SiteVision system when paired with a Catalyst DA2 receiver. It also says SiteVision can show underground services. Another option is visual positioning. Google says its ARCore Geospatial API matches the camera view with a model built from Street View imagery. As a result, it can be far more accurate than GPS alone, though it only works where Street View coverage exists.

Why does GIS data quality decide whether AR can be trusted?

Data quality decides trust because AR shows your records with great confidence, whether they are right or wrong. In fact, a neat 3D pipe on screen can look more certain than the paper plan it came from.

For example, one useful tool is the ASCE 38 standard for subsurface utility data. It sorts each record into four quality levels, from desk records up to exposed and measured assets. So if you tag each asset with its level, the AR app can show crews how far to trust each line. For instance, it could draw verified lines in solid colour and record-only lines as dashed.

For instance, Malaysia shows why this matters. A 2015 FIG paper by JUPEM staff says the agency set up its utility mapping work in 2006 after a Cabinet directive. That directive followed frequent water and power cuts caused by damage to pipes and cables during digging. The same paper reports that much provider data failed JUPEM’s accuracy standards during field checks. Also, it notes a 2014 decision that new utilities must be surveyed by licensed land surveyors during installation.

Therefore, run a data check before any AR pilot. Look for missing depth, missing size, broken links and lines that cross buildings. Our GIS data QA/QC checklist for utilities sets out the tests we use.

How does AR help prevent dig damage to buried utilities?

AR helps prevent dig damage by showing crews where assets should be before the bucket hits the ground. But it works as one layer of defence, next to one-call checks, locating and potholing.

Yet dig damage is still common in mature markets. For example, the Common Ground Alliance’s 2024 DIRT report for North America analysed 196,977 unique damage reports. In addition, the top ten root causes made up 85 percent of reported damages. Several of these causes are ones a good AR view can support.

So where does AR help most? First, it shows the line during digging, even after paint marks fade or vanish. Next, it lets a supervisor check marks against records and spot a missed asset. Then it can warn when a planned trench crosses a high-risk line. However, the same report lists incorrect records or maps as a root cause. That is a reminder that AR spreads bad records just as fast as good ones.

What are the main use cases for AR in utility field work?

The main use cases are dig planning, locating support, maintenance, inspection and as-built capture. In each case, AR saves time because crews see the network in place instead of on a plan.

As-built capture deserves special attention. Each time a crew exposes a line, they can log its true position and depth. As a result, that asset moves up to a higher quality level, and the next AR view gets better. Our guide to mobile GIS field data collection for utilities covers how to build that loop.

Also, AR fits well with a utility digital twin. The twin holds the 3D network, and AR brings that model to the street. For water networks in particular, see our water utility GIS page.

Is your utility ready for an AR field pilot?

A utility is ready for an AR pilot when it has usable GIS data for one area, a clear field task and a plan for positioning. You do not need a perfect network model to start. But you do need to know where the gaps are.

Then pick one network and one district for the pilot. Measure simple things, such as time to find a valve or the number of marks that differ from records. If you need help closing data gaps first, our field data collection team can survey assets to a known accuracy.

Frequently Asked Questions

Is augmented reality for utilities worth adopting now?

Yes, for most utilities with a working GIS, AR is worth a focused pilot now. The tools have matured, and high-accuracy positioning is easier to get. But the return depends on your data, not on the headset.

So start small. First, clean one area and tag each asset with a quality level. Next, choose the right positioning for the task. Then train crews to read the view and keep locating and exposure in the process. Finally, feed every field fix back to the GIS. RedPlanet’s augmented reality for asset management service can help you plan and run that pilot, from data checks to field roll-out.