In our latest Geospatial Insight blog, Joe Billing - one of Getmapping’s leading Geospatial Technology Specialists – casts a spotlight on LiDAR technology, providing an insight into the journey that LiDAR Technology has taken from simply measuring distances to now underpinning some key technological transformations, and how in the future LiDAR will combine with other emerging technology such as AI to deliver powerful solutions to some of the challenges and opportunities that the world is facing. What is LiDAR?
LiDAR, as many in the geospatial and remote sensing industries can tell you, is simply an acronym for Light Detection and Ranging1, similar in spirit to RaDAR or SoNAR. Where the latter two technologies focus on radio waves or sound propagation, the light used in LiDAR is that emitted from the humble laser.
The key idea behind the technology is to use light to determine a distance to a remote object. Repeat this millions of times a second and we can produce what is commonly referred to as a point cloud.
This is by no means a new concept and the technology can trace its roots back to the 1930’s, when early pioneers were using searchlights and pulses of light to measure the height of clouds.
The advent of the laser in the 1960’s saw airplanes start to be used as a platform for the laser. By the 1980’s, satellite positioning systems were introduced and provided a reliable solution for determining the aircraft (or rather the laser’s) position. This was then also later supplemented by inertial measurement units (IMU) to give rise to the framework for the mapping LiDAR systems of today. Fast forward to present day, and with the mainstream adoption of drones taking centre stage, the question becomes not what is LiDAR, but what, if anything, has changed regarding LiDAR technology?
Apart from small incremental refinements, it’s probably safe to say the technology has somewhat stabilised over recent years. The most obvious changes that come to mind are that of miniaturisation and increased capability. This follows the trend of just about all electronic-based equipment over time. One only need look at the computer of yester year, which easily filled an entire room, requiring several skilled operators. Today, we all carry far more sophisticated devices in our pocket and need only the dexterity of an agile thumb to operate.
The use of unmanned aerial vehicles (UAV’s) as a platform has driven the race for smaller, lighter, and more cost-effective LiDAR scanners. This has had a positive effect on the increased use of handheld, SLAM based scanners as well. Toshiba have developed what it claims is the smallest LiDAR in the world, boasting a range of 300m. The system is aimed at autonomous driving, but can be configured to include:
Mapping
Drones
Infrastructure monitoring
Surveying
Robotics and more
Toshiba’s small form factor LiDAR with 300m range.
Another example is Apple’s iPhone and iPad, which now feature LiDAR scanners built in as standard feature on some models.
LiDAR incorporated into your mobile phone.
The knock-on effect is that the once cumbersome, large, complex, and specialised technology of LiDAR has been bumped off its perch. No longer is it purely the domain of the highly qualified specialist. It, along with many other specialist technologies, has become democratised into an everyday tool, accessible to anyone willing to give it a go.
Granted, not all LiDAR scanners are created equal. Along with this, LiDAR has numerous applications, many outside of a geospatial context. Range, noise, accuracy, scan rate, resolution etc. will always be differentiation factors. It’s a case of understanding the application and being able to choose the right tool for the job.
Dealing with data
LiDAR has always been known as a solution which 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 which contain greater variety, arriving in increasing volume and with more velocity.
In other words, large complex datasets where traditional processing software are unable to deal with them.
For some users unfamiliar with LiDAR, this has been a hindrance to adoption. In the past, consuming LiDAR datasets of significant size, end users typically request 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 once previously complex, tedious, and quite manual processing tasks, left to that of 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 find themselves to be very complimentary. 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 which 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 the reasons for using the technology are just as widespread and discipline specific. Overarching to this though, is the use of the technology to achieve clearer insights to build a better tomorrow!
Why do we do it?
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 we serve 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.
Bibliography
https://en.wikipedia.org/wiki/Lidar https://www.britannica.com/technology/lidar https://en.wikipedia.org/wiki/ENIAC https://electronics360.globalspec.com/article/18318/world-s-smallest-lidar-can-detect-a-range-of-300-meters https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/images/iPhoneLidar/iphone-13-pro-max-cameras-01.png https://www.oracle.com/za/big-data/what-is-big-data/ https://acicorporation.com/blog/2022/03/09/lidar-mapping-and-ai-tech-forge-a-promising-bond-for-our-future/#:~:text=Advancements%20in%20AI%20technology%20have,grounds%20using%20airborne%20point%20clouds https://www.oracle.com/za/artificial-intelligence/what-is-ai/ https://www.getmapping.com/gsaas-solution
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