LiDAR: A Technology Driving Transition

In our latest Geospatial Insight blog, Joe Billing (one of Getmapping’s leading Geospatial Technology Specialists) casts a spotlight on LiDAR technology. He explores its journey from simply measuring distances to becoming a key enabler of technological transformation. Looking ahead, LiDAR is set to combine with emerging technologies like AI to deliver powerful solutions to global challenges.

What Is LiDAR

LiDAR, short for Light Detection and Ranging, is similar in principle to radar or sonar. However, instead of using radio waves or sound, LiDAR uses laser light to measure distances.

The core idea is simple: use light to determine the distance to a remote object. Repeat this millions of times per second, and you generate what’s known as a point cloud.

A Brief History of LiDAR

The concept of using light to measure distance dates back to the 1930s, when searchlights and light pulses were used to estimate cloud heights. The invention of the laser in the 1960s revolutionized this approach, enabling airborne LiDAR systems. By the 1980s, satellite positioning and inertial measurement units (IMUs) were integrated, laying the foundation for modern mapping-grade LiDAR systems.

The Impact of Drones and Miniaturisation

Today, drones have become a popular platform for LiDAR, driving demand for smaller, lighter, and more affordable scanners. This trend mirrors the broader evolution of electronics, where once-room-sized computers now fit in our pockets. Similarly, LiDAR systems have become more compact and accessible.

LiDAR in Everyday Devices

Miniaturisation has led to LiDAR being integrated into consumer devices. Toshiba, for example, has developed a LiDAR unit with a 300m range, aimed at autonomous vehicles but adaptable for:

• Mapping.
• Drones.
• Infrastructure monitoring.
• Surveying.
• Robotics.

Apple has also incorporated LiDAR into some iPhone and iPad models, making the technology more mainstream.

Toshiba’s small form factor LiDAR with 300m range.

LiDAR incorporated into your mobile phone.

From Specialist Tool to Everyday Tech

LiDAR is no longer confined to experts. As the technology becomes more user-friendly and affordable, it’s being adopted across industries and by individuals. This democratization has transformed LiDAR into a versatile tool for a wide range of applications.

Not All LiDAR Is Created Equal

Despite its growing accessibility, LiDAR systems vary significantly in terms of:

• Range.
• Accuracy.
• Noise levels.
• Scan rate.
• Resolution.

Choosing the right system depends on understanding the specific needs of your application, whether it’s for geospatial mapping, autonomous navigation, or infrastructure monitoring.

Dealing With Data

LiDAR has always been known as a solution that captures huge amounts of data. With improving scan rates, increased point densities, and faster and larger storage devices, the problem of dealing with big data is ever-increasing. Oracle defines big data according to the three V’s. These are data that contain a greater variety, arriving in increasing volume and with more velocity. In other words, large complex datasets that traditional processing software is unable to deal with.

For some users unfamiliar with LiDAR, this has been a hindrance to adoption. In the past, when consuming LiDAR datasets of significant size, end users typically requested nearly all the value be stripped away to fit in with conventional data and software capabilities.

However, Software vendors have begun to respond to address this. What was previously complex, tedious, and quite manual processing tasks, left to the specialist, is now significantly simplified and automated. But there is still a price for this automation, and that is the overhead of computer hardware and infrastructure to do the heavy lifting. With increases in CPU, GPU, and more memory-hungry software, comes the need of being able to have powerful machines or cluster-based computing power. This comes at a premium, and so cloud-based computing as a service has been born to offer workarounds. Here, a variety of options are available for permanent use or fixed time.

The Age of Artificial Intelligence

Today, with the emergence of artificial intelligence (AI), machine learning algorithms, and computer vision, dealing with LiDAR data is becoming ever more automated. In simple terms, artificial intelligence can be thought of as systems or machines that mimic human intelligence to perform tasks iteratively and can improve themselves based on the information they collect. A prime example of this in action is self-driving cars.

What Can AI Do for LiDAR

LiDAR and AI are two very different technologies, but they find themselves to be very complementary. AI can assist with the detection of real-world objects within a point cloud. Similarly, a point cloud, along with computer vision, gives an AI the ability to “see” and adapt. From a geospatial point of view, this technology greatly simplifies registration processes along with classification and data extraction. Items such as street furniture, road signs, powerlines, houses, water bodies, vegetation, vehicles, etc., can all be identified in a fraction of the time and more accurately than before. The benefit is better datasets, quicker turnaround times, and lower costs to the end user.

The Future

While technologies like LiDAR and AI will continue to incrementally evolve, the future lies not in individual technologies like LiDAR or AI. The future is not hardware or software. Rather, the future is the combination of these technologies, both hardware and software-based. The future is the ability to draw insights from data using various tools and sensors to provide meaningful and beneficial solutions, which ultimately improve everyday life. The future lies in investing in diversity, both in the workforce and the equipment that enables productivity.

LiDAR has many applications, and there are likely many more to come. Some of these include insurance risk analysis, policing, medical applications, reverse engineering, autonomous driving cars, and volumetric analysis. Given the diversity of these example applications, it is not hard to imagine that the reasons for using the technology are just as widespread and discipline-specific. Overarching this, though, is the use of the technology to achieve clearer insights to build a better tomorrow.

Why Getmapping Uses LiDAR

At Getmapping, our core focus is solving the problems that face our planet from a geospatial perspective. We employ LiDAR as a tool on an everyday basis. Coupled to this, we provide our Geospatial as a service (GSaaS) solution, providing organisations with the ability to easily access the ideal geospatial data for their needs.

Some of the more traditional industries Getmapping serves include:

• Municipalities and local authorities.
• National Road Authorities.
• Archeology.
• Mining.
• Forestry.
• Electricity Utilities.
• National mapping agencies.
• Software vendors.

Moreover, as the lines between geospatial and non-geospatial applications become ever increasingly blurred, we foresee that our solutions will provide insight into many areas of industry to help turn data into business information. The future is a world where intelligent solutions become reality and analytics and insight drive innovation. Alarmingly, this future is already here, and so our challenge remains to continue providing new and innovative solutions to solve the challenges that face our planet.

Joe Billing, Manager, Geosense

Joe works as a manager at Geosense, a wholly owned subsidiary of Getmapping, based in both Cape Town and Pretoria, South Africa.

Joe’s focus, currently, is on oblique imaging solutions along with various LiDAR applications, including aerial, terrestrial, and mobile. Joe has been actively involved in the geospatial industry, having held various roles since 2000, and has been a member of the Getmapping/Geosense team since 2014.