At INTERGEO 2025 in Frankfurt, FARO®, a business of AMETEK®, Inc. wasn’t just celebrating a milestone, it was making a case for how 20 years of terrestrial laser scanning experience feeds directly into the next wave of GIS: mass data, AI-ready point clouds and digital twins that help societies manage infrastructure at scale.
“All of the change in the world requires measurement,” said Kenneth Smillie, business development lead for FARO’s geospatial business. “My view on the industry is that we measure change… and measuring change efficiently is going to be using mass data, and mass data is laser scanning.”
That simple statement—we measure change—is a neat summary of why FARO’s hardware, software and acquisition strategy matter to GIS professionals right now.
From Open-Pit Surveyor to Mass-Data Advocate
Smillie’s own path mirrors the industry’s shift from point-based to mass-data workflows. After a geomatics degree, he started as a field land surveyor in open-pit mining, then moved into project management and aerial survey in the UAE, sales of airborne sensors at Leica Geosystems/Hexagon, and business development with pioneer van-based mobile mapping company 3D Laser Mapping.
“In 2018 I joined GeoSLAM,” he recalled, “and I was a sales director with them until the acquisition of GeoSLAM by FARO in September 2022. Since then, I’ve been working on the sales side as business development for primarily the geospatial part of the business.”
That journey from total stations and kinematic GNSS through mobile mapping and handheld scanners puts Smillie in a good position to judge what’s actually changing on the ground.
20 Years of Terrestrial Scanning—and a 400-Meter Push into Geospatial
2025 marks two decades of FARO terrestrial laser scanners.
“Interestingly enough, this year is the 20th year that FARO has been selling and producing terrestrial laser scanners,” Smillie said. “We actually have on the stand here at Intergeo one of the original scanners that was brought out in 2005, leading up, of course, to today.”
On the AEC/geospatial side, FARO’s flagship remains its Focus series of terrestrial laser scanners, recently refreshed and extended:
“The main product line up at the moment is the terrestrial laser scanner, which is the Focus, which has three models, recently refreshed, particularly with the 400-meter range, which, of course, is a little bit more useful for the geospatial industry.”
For AEC, the shorter-range Focus models still dominate. But as FARO pushes deeper into surveying and mapping, the long-range Focus Premium Max is positioned as an explicitly geospatial scanner: same accuracy, same workflows, but with reach.
That’s one quantitative reason FARO matters to GIS: it has taken a mature AEC scanning platform and tuned it to the operational ranges and accuracies geomatics professionals expect.
Blink, GeoSLAM and the Imagery-First User
FARO’s acquisition of GeoSLAM opened the handheld/mobile segment—topographic mapping, forestry, outdoor scanning—while the new Blink™ imaging laser scanner targets facilities management and construction progress.
“The most recent introduction has been the Blink Scanner,” Smillie said. “Which is more for the FM and AM type customers… used again in interiors, construction progress mapping, primarily with the imagery—superb clarity on the 360 imagery… but with the addition of a LiDAR sensor.”
That design is intentional: Humans interpret the world through images. Machines and GIS workflows rely on accurate 3D geometry.
“We are visual humans,” Smillie said. “So, the imagery has primary recognition. But of course, now we back it up with great point clouds, with which we can do accurate measurements.”
He tied this back to the early van-based mobile mapping systems: “Those systems… had high accuracy, long range laser scanners. But they also had multiple cameras… What we found there was that to identify certain assets, it was somewhat easier to use the imagery… The point cloud in the background was used for actual measurements.”
The pattern is the same today: Imagery up front for asset identification and inventory. Point cloud in the background for measurement and modeling.
As Smillie put it: “You can’t have just one or the other now. You must have the combination of both.”
SCENE as the Data Backbone: Clean, Registered, AI-Ready
Hardware doesn’t matter if the data never gets clean enough for downstream use. For FARO, that’s where SCENE or Sphere XG comes in. “The standard platform that all of our sensors are now able to feed into is the FARO Scene software,” Smillie explained. “We can take disparate point clouds from either our hardware offering or even data that’s been given from another source, maybe a UAV, and that information can then be registered within SCENE. Additionally, with the ever-expanding cloud-based capabilities of our Sphere XG platform, we also offer customers a choice between processing data online or offline, offering them more choice on their way to deliverables.”
What SCENE & Sphere XG does in practice:
- Registers multiple point clouds and trajectories
- Cleans data, checks integrity and accuracy
- Overlays imagery for visualization and colorization
- Exports into common formats (LAS/LAZ, E57, etc.) for vertical applications
“Our goal and our job,” Smillie said, “is to provide the data that has already been cleaned, checked, and the integrity is there, the accuracy is there, and be able to output that to whatever the customer’s application is.”
For GIS users, that means FARO is not trying to own every workflow; instead, it’s positioning SCENE as a pre-processing backbone feeding into specialized FM, topo, corridor, or construction analytics tools. It’s not just about sensors, it’s about making sure you have interoperable, trusted input data for AI and GIS.
AI, Machine Learning and Why Accuracy Suddenly Matters Even More
When the conversation turned to AI, Smillie immediately linked it to data volume and accuracy.
“Mass data collection through laser scanning is one of the things that is going to be necessary,” he said. “High quality point cloud data is going to be necessary for AI and machine learning.”
He drew a direct analogy to large language models: “They work primarily by having large volumes of information taken in to then interpret and come out with the right answer. I think it’s similar for AI in the geospatial industry… the input is going to have to be very accurate LiDAR point clouds and, for certain interpretation, imagery as well.”
This is where FARO’s long-term scanner experience, multi-platform coverage and preprocessing stack intersect:
- Mass data: terrestrial, handheld, mobile and image-rich systems create dense coverage of assets and corridors.
- Accurate data: survey-grade point clouds underpin alignment, modeling and deformation analysis.
- Structured data: Scene and standard formats keep everything usable downstream.
Smillie sees near-term gains in automatic feature recognition and classification.
“I believe that at the future is automatic feature recognition, automatic classifications… asset inventory indoors, and a growing number of feature classifications that are automatically captured externally, like street furniture and pavement. The number of classification areas that can be captured automatically is somewhat limited at the moment, but I think with the advent of AI and machine learning we’ll soon be able to automatically classify infrastructure in the outside world.”
For GIS and asset management teams, that’s the payoff: systems that don’t just collect point clouds, but actually feed automated GIS attribution at city, network or enterprise scale.
Platforms and Payloads
Smillie also painted a picture of where the scanners will physically live in the next few years, and it’s not just on tripods.
“I think we’ll see an industry moving forward where there are various platforms such as robotics,” he said. “We’ve got, of course, a lot of UAVs at the moment, but we’ve also got quadrupeds and even humanoids. We’ve got vehicle-based systems. We’ve got boat or water-based systems.”
For GIS professionals, that translates into:
- More continuous coverage across infrastructure networks
- Faster revisits for change detection
- Richer, multi-temporal datasets for digital twins and spatio-temporal analysis
Digital Reality, Rework and the Value to Taxpayers
Asked how “digital reality” translates into real-world value, Smillie pointed to large construction and infrastructure projects.
On stadiums and similar major builds in the UK, he said, “If we start from a digital perspective from the ground up, we’re avoiding issues like rework. It’s well known in the construction industry that the percentage of rework is unreasonably high, which, of course, has a cost implication, budget overruns through time and money.”
Laser scanning and digital twins help catch clashes early: “Construction progress will improve efficiency, and ultimately means that projects are completed on time and on budget.”
He was even more pointed when discussing aging infrastructure:
“I looked at a report recently that said something approaching 55.9% of bridges in the U.S. are either fair or poor condition.
Many of those bridges were built with 50-year lifespans, and dams between 50 or 100-year lifespans, depending on when they were built (from the early 1900s), and are now at or beyond that horizon. The first step is simply knowing what you have.
“The starting point is, what do we have now?” he said. “3D modeling of those types of infrastructure is used as a starting point, then designers can come in and say, okay, we’ll need to take this part away, put in new steel work, and so on.”
From there, digital workflows carry through rehabilitation to completion. “You’ll get the renewed infrastructure at the lowest possible cost, and hopefully with the least amount of disruption,” Smillie said. “That’s one of the things I think that we are contributing to society at the moment.”
For GIS professionals, that all adds up to much more than a new scanner on the show floor. FARO brings two decades of terrestrial-scanning experience into a mature Focus line that now reaches 400 meters, extends that heritage across handheld, mobile and imagery-first systems, and ties it together with Scene as a preprocessing backbone that delivers clean, registered, standards-compliant point clouds into whatever GIS or FM stack you already run.
The company’s hardware, whether static or mobile, is very well suited to feeding a universally compatible mass-data pipeline rather than a closed ecosystem, and its roadmap assumes that AI will depend on exactly the kind of dense, accurate, image-rich point clouds FARO is trying to make routine.
Most importantly, the company is aiming that capability at real problems like rework, cost overruns, aging bridges and dams. In that sense, FARO is positioning itself less as a scanner vendor and more as a mass-data provider for GIS and digital twins, with 20 years of scanning experience now feeding directly into AI, automation and infrastructure-scale decision-making.