LiDAR Data Collection 101
01. What Is LiDAR?
LiDAR, short for Light Detection and Ranging, is a remote sensing technology that uses light pulses to measure ranges and produce 3D imagery. The industry also refers to it as LADAR or laser altimetry.
Since the early 1900s, LiDAR technology has been used to scanning surfaces, such as roadways and buildings, to create highly accurate topographical models called digital elevation models (DEMs). As the technology developed, so did its applications. Today, LiDAR sensors are present in spacecraft, planes, cars, robots, drones, and, most recently, smartphones, such as the Apple iPhone 12 Pro and iPhone 13.
These days, it’s becoming more popular in newer self-driving vehicles, including the Tesla Model 3. It measures the light pulses so it can compute the distance to an object, while the rotating mirror scans your vehicle’s surroundings to create a three-dimensional image of those surroundings.
However, making cars safer merely scratches the surface of the LiDAR system’s applications in our modern world. For example, LiDAR can help governments, organizations and corporations understand a variety of complex issues, including:
- How much energy they need from power plants,
- How much water they will need to irrigate crops during drought conditions, or
- How much carbon dioxide there is in the atmosphere.
LiDAR works by firing a laser at an object and measuring the time it takes for the light to return. It calculates the distance based on how long it takes for the light to bounce off an object and return to a sensor. The more detailed your LiDAR system, the more precise this signature will be.
02. How LiDAR Works
Businesses most commonly use LiDAR for surveying, navigation and mapping. The technology emerged from the desire for a cost-effective alternative to radar for measuring distance. At the heart of a LiDAR system is an infrared laser, which emits pulses of light. When surveyors direct the laser beam at an object, light bounces off the object, which the LiDAR system’s detector then collects. To determine the distance between the system and the object, surveyors measure the time between the outgoing pulse and the incoming signal. LiDAR systems typically measure distance in inches or centimeters.
There are two main types of LiDAR:
- Terrestrial – Businesses typically use terrestrial LiDAR to create maps and models of the surrounding environment. Users can mount it on self-driving cars and drones, but they don’t use it the same way as aerial LiDAR.
- Aerial – You can mount Airborne, or aerial, LiDAR on an aircraft and use it to create high-resolution topographical maps and imagery.
Surveying and Mapping
Surveyors and researchers use LiDAR to capture aerial imagery and create 3D maps and models of a specific area. To validate its accuracy, they typically pair LiDAR data with terrestrial surveying techniques. This technology allows researchers to gain a detailed understanding of their surroundings that they would not get with remote sensing alone. LiDAR maps and models are particularly useful in disaster response situations. During disasters, such as hurricanes or wars, aerial imagery and topological maps help emergency personnel assess the damage and assist with relief efforts.
LiDAR is crucial to self-driving cars, drones and other autonomous vehicles. By creating a real-time map of the environment, LiDAR helps autonomous vehicles navigate their surroundings safely.
Self-driving cars, specifically, use several sensors to detect their surroundings and navigate the world. In contrast to cameras, which only detect the brightness of objects, LiDAR sensors actually detect the distance between the vehicle and other objects.
Alternative sensors, such as ultrasonic and radar, also detect objects but, unlike LiDAR, do not give depth information. This means that a self-driving car will know what an object is but not how far away it is. LiDAR can detect both the object and its distance from the car. The combination of these sensors allows self-driving cars to create a real-time, 3D map of their surroundings. This is crucial for safe driving and will help drivers navigate busy streets, tricky intersections and difficult weather. The car can use this map to identify safe places to stop and wait when traffic is too heavy to drive.
How Accurate Is LiDAR Remote Sensing?
LiDAR technology is extremely accurate, making it a useful tool in construction, city planning, disaster relief management, agriculture, mining and the military. It can measure the distance between the system and an object with a precision of less than an inch. However, it is not perfect.
Many factors can affect LiDAR data’s accuracy, including:
- The weather. Weather conditions, such as fog, rain, or snow, can negatively affect accuracy.
- The system used. LiDAR systems also vary in quality, making some less accurate than others.
- The quality of data. LiDAR data can also have other errors, such as incorrect scaling and representation.
Despite the anomalies, LiDAR remains one of the most accurate geospatial technologies around.
Issues With Current LiDAR Technology
While LiDAR is effective, there are still some issues with its current form. One of the major problems with LiDAR is the amount of energy required to power and operate the system compared to other sensors. The major challenge is that the laser generates large amounts of energy, and most detectors require large amounts of energy to operate. To reduce the amount of energy needed, LiDAR systems have to be smaller, which can lead to decreased accuracy.
Another issue is the cost. LiDAR sensor costs vary depending on the manufacturer, type of sensor and if it integrates into a larger system, such as a drone, smartphone or car. A quality entry-level LiDAR sensor can cost as much as $23,000, while high-end LiDAR systems can cost $50,000 or more.
LiDAR sensors are incredibly accurate, but they are also incredibly expensive. This is one reason that autonomous vehicles are not yet widely available. LiDAR sensors are currently costly to produce, and their current designs require large sensors installed on top of vehicles, which are not practical for driverless cars. New LiDAR technology is in the midst of a revolution that will see smaller, less expensive sensors become commonplace.
03. Two Main Ways to Collect LiDAR Data
What makes remote sensing technology such as LiDAR incredible is the detailed data it collects. As we mentioned in chapter one, the real-world applications of LiDAR include an evolving list of industries, from oceanography and architecture to autonomous vehicles. But how exactly is LiDAR data collected?
How Is LiDAR Data Collected?
LiDAR systems shine light toward the ground and measure the amount of time it takes for the light to bounce back. The timing of the light’s return reveals how far away the ground is. By scanning the laser across a wide area, a LiDAR unit can create a detailed map of the ground.
The previous chapter gave a brief overview of the two main categories of data collection for LiDAR remote sensing. In this chapter, we’ll delve a little deeper into both.
Terrestrial LiDAR SystemsTerrestrial LiDAR systems are typically used to create maps and measure the exact distance between two points. The systems have three main parts: a rotating laser, a rotating mirror and a set of sensors. The laser shines onto the rotating mirror, which reflects the light onto the ground. The sensors detect when the light reflects off an object, and the computer records the distance.
As the name suggests, terrestrial LiDAR systems shine the laser toward the ground. In fact, we call them terrestrial because they are meant to measure objects on the ground, not in the air. The sensors mount on a vehicle that drives across the terrain. The LiDAR sensors rotate as the vehicle travels, collecting data from all sides.
We can divide Terrestrial LiDAR into two categories:
- Mobile LiDAR. Mobile LiDAR systems are terrestrial LiDAR systems that mount onto a moving vehicle, such as an autonomous car. Not only do these systems collect data to create an accurate 3D model of an area, but they can also track surrounding vehicles or objects. Mobile LiDAR also works to avoid collisions, either by sensing other vehicles or reporting their location to the car’s operator. Mobile LiDAR systems are more expensive than terrestrial LiDAR systems, but they can also collect more data. One reason for this is that mobile LiDAR can collect data from multiple angles, rotating 360 degrees on the vehicle.
- Static LiDAR. A static LiDAR system is a single-point system. You can mount static LiDAR systems on a moving vehicle or a stationary surface. Static LiDAR systems can be high-resolution or low-resolution, but businesses always use them for high-accuracy scans.
Aerial LiDAR SystemsOn the other hand, aerial LiDAR systems are meant to scan the ground from the air. They are typically mounted on an aircraft, although you can also find them on satellites. Aerial LiDAR systems create topographical maps of areas, such as flood plains and forests, that are difficult to access using ground-based systems.
They use rotating gimbals with a laser and sensors to scan the ground. The laser and sensors mount on the gimbals, which you install on an aircraft. Aerial LiDAR systems, much like terrestrial LiDAR units, rotate as they collect data.
Aerial LiDAR systems can collect data from as low as 100 feet above ground level (AGL) or as high as 60,000 feet, depending on the system. There are three major types of aerial LiDAR systems:
- Single-rotor. Single-rotor systems are the cheapest option and you usually use them for topographic surveys. While the data they collect is accurate, it’s not very detailed.
- Twin-rotor. Twin-rotor systems are used for high-resolution surveying, collecting data with a wide swath.
- Fixed-wing. Fixed-wing systems are used for high-precision photogrammetry.
The type of aerial LiDAR system used depends on the specific needs of the project.
There are two key categories of aerial LiDAR:
Topological LiDARTopological LiDAR is like static terrestrial LiDAR but you use it to survey much smaller areas. Surveyors and researchers use topological LiDAR on buildings or even single roads. Then, they use the data collected to create a 3D model of the surrounding area. Topological LiDAR also collects data that is used for navigation, collecting elevation, slope and other information about the environment.
Bathymetric LiDARBathymetric LiDAR is for collecting data about the ocean floor. Users lower a LiDAR sensor from a boat or ship to collect data. Bathymetric LiDAR is used in a wide range of industries, including offshore drilling, fisheries and underwater archeology.
These are just a few examples of the different LiDAR systems. Although these are the most common, there are many other LiDAR systems. The best way to learn more about LiDAR is to get involved with research or even take a class.
04. Understanding the LiDAR System
LiDAR sensors send out pulses of infrared or ultraviolet light that reflect back from surfaces they hit, such as buildings and vegetation. A time-of-flight, or TOF LiDAR, then measures how long it took wavelengths of infrared or ultraviolet radiation to travel between the emission point and detection point using electronics linked with photodetectors.
It can modulate an optical pulse in terms of intensity (brightness) or frequency, allowing one pulse per range measurement. However, most systems use multiple pulses at once so that they can gain all required data simultaneously while minimizing error.
The LiDAR system also calculates the distance by timing how long it takes for each pulse it sends out from one side versus another side where both sides detect reflections from the same target — essentially calculating the difference between two times multiplied together.
The LiDAR sensor provides the most accurate 3D measurements of your target area. You can use these measurements to create detailed 3D models and point clouds used in many fields, including:
Geomorphology (the study of landscapes)
Land management and conservation
Why Use LiDAR?
You can use LiDAR to measure anything that has a surface area. Because it can penetrate through fog and other inclement weather conditions, LiDAR is also ideal for gathering data in areas that are hard to reach by other means (such as satellites).
Unlike traditional survey methods such as GPS or photogrammetry that rely on visual cues (or images), LiDAR technology is perfect for mapping out large areas such as forests or cities — places where taking pictures would be impossible because of dense vegetation or tall buildings blocking sunlight — as well as smaller areas such as bridges over rivers.
The most common use of LiDAR is to map the Earth, ocean and atmosphere. NASA also used it to map the moon’s surface. It deployed a special LiDAR called “lunar topographic mapping” on lunar landers and rovers, allowing scientists to create 3D maps that help us better understand our place in space.
Besides its uses in cartography, you can use LiDAR for geology research as well — a process known as remote sensing or remote sensing technology (RS). This technology allows scientists to analyze objects at a distance without having direct access for observation via satellite imaging techniques such as high-resolution radar or ultraviolet light photography.
Versatile Data Collection
LiDAR is an effective and versatile method for 3D mapping and surveying. It’s a multifaceted tool with many applications. From use by the military for targeting to helping scientists understand geological data, there are many possibilities.
05. Discrete vs. Full Waveform LiDAR
The way we perceive the world is constantly developing. We’re no longer reliant on sight alone to understand our surroundings. Instead, new technologies such as LiDAR are helping autonomous vehicles “see” in ways that were not previously possible or safe. But how do these two types of technologies work? What are the pros and cons of each? And which is right for your business? The answer to that last question might surprise you. That isn’t always the case. Both have their unique advantages and disadvantages, which we will explore in this chapter. Read on to learn more about full-waveform vs. discrete LiDAR technology, including details about where each excels and where improvements can be made.
What Is Full-waveform LiDAR?
Full-waveform LiDAR or F-LiDAR is a type of scanning technology used for mapping and autonomous vehicle perception. This type of LiDAR uses photons, or light, to generate digital images of the surrounding environment. An F-LiDAR system typically comprises an emitter and a receiver. It fires a laser in a specific direction. The receiver collects photons that bounce off objects in the environment and sends the data back to the computer. The computer then analyzes the photons to create an image.
For example, self-driving cars use these images to detect objects, navigate roads and make decisions. F-LiDAR differs from discrete LiDAR because it can create a continuous 360-degree image of the environment. Discrete LiDAR uses a series of short laser pulses to create a 2D image.
What Is Discrete LiDAR?
A discrete LiDAR system uses laser pulses to create a 2D image of the environment. It sends out the pulses in quick succession, with each pulse illuminating a specific area. As each pulse reflects off of different objects, it reduces its intensity. The system then records the intensity of each pulse as well as the time it took for the pulse to reflect onto the sensor. The computer then uses this data to create a 2D image of the environment. Discrete LiDAR systems typically emit between 10,000 and 100,000 laser pulses per second, each lasting about 10 microseconds.
There are two main types of discrete LiDAR systems:
Quasi-interferometric. Quasi-interferometric systems use two laser beams projected in opposite directions. The beams then reflect onto a sensor, where the system compares them to one another to create an image.
Interferometric. Interferometric systems have one laser beam that they reflect off an external object and then project back to a sensor.
Advantages of Full Waveform LiDAR
Full-waveform LiDAR systems can create a continuous image of the environment, making it easier to create a comprehensive map of the surroundings. This is especially helpful when the autonomous vehicle is moving. Full-waveform LiDAR systems can also detect objects further away and at a higher resolution than discrete LiDAR systems. This makes them better at detecting small objects, such as pedestrians, animals and bicycles.
These systems also have a higher accuracy rate than discrete LiDAR. This is because each pulse from the discrete system has a specified range. If an object is outside of that range, the laser won’t detect it. However, with full-waveform LiDAR, it uses data from all the photons to create the image, regardless of their range.
Advantages of Discrete LiDAR
Discrete LiDAR systems are easier to manufacture than full-waveform systems. This makes them cheaper and more accessible than their counterparts. Discrete LiDAR systems can also create images at a faster rate than full-waveform systems. This is because each pulse lasts for 10 microseconds as opposed to the 10 milliseconds it takes a photon to bounce off an object and return to the receiver.
Although discrete LiDAR systems can create images at a higher rate, they often have a lower resolution. This means that it will detect objects near the vehicle but not at a high enough resolution to be useful for the autonomous vehicle. Discrete LiDAR systems are also able to detect objects at a longer distance than their full-waveform counterparts. This means that they can detect objects further away and may help avoid collisions.
Which One Is Better for Businesses?
Full-waveform and discrete LiDAR systems have their own set of advantages. They each have a specific use case in the autonomous vehicle industry. Full-waveform LiDAR is better at creating a highly detailed map of the environment. This feature makes it ideal for commercial mapping applications. On the other hand, discrete LiDAR is better at detecting objects in the near-field, or the area within a few meters of the vehicle. This makes discrete LiDAR better for autonomous vehicles that operate in an urban environment.
That said, both technologies have room for improvement. Full-waveform LiDAR systems are better at creating detailed maps but are less accurate than discrete LiDAR systems. Discrete LiDAR systems are better at detecting objects near the vehicle but are less detailed than full-waveform LiDAR systems.
06. LiDAR File Formats
LiDAR data is an extremely important asset in any survey project. The quality and usability of the LiDAR you collect determine how successful your final survey results will be. To get the most out of your LiDAR, it’s important to choose the right file format to suit your survey project needs. There are a wide variety of different LiDAR file formats available on the market today, each with its unique attributes. Here are some of the best LiDAR file formats for your surveying project:
Storing LiDAR Data
LiDAR data is large and complex, so you must store it in a format that can accommodate its unique properties. Several file types are ideal for housing LiDAR data and maintaining its accuracy:
CAD (Computer-Aided Design) – Architectural and surveying software create CAD files, which means that they can contain LiDAR data and other survey-relevant information.
GRD (Geographical Resources Designation) – GRD files are a standard data format used in the GIS (Geographic Information System) industry.
ESRI (Environmental Systems Research Institute) – ESRI file types, such as the shape file, are commonly used for storing and accessing LiDAR data.
Point Cloud – A point cloud file type is an ideal storage format for collecting and storing LiDAR data.
TIFF – A TIFF file type is often used for LiDAR data and other 3D imagery.
Point Cloud LiDAR File Format
With a point cloud LiDAR file format, it collects data points in LiDAR format and then packages them together for easy storage. Most point cloud LiDAR formats are compressed, which makes them ideal for storing large amounts of data on a single computer. A point cloud LiDAR file format is also grid-based and organized in rows and columns, which makes navigation and manipulation simpler.
A point cloud LiDAR format is ideal for storing high-quality data in a compressed and organized format. However, it’s not the best file type for transferring data to clients or other parties.
LAS File Format
A LAS file format is a compressed file type that can store raw LiDAR data. This file type includes all the raw data that you collected during the LiDAR survey, but it does not include any of the information about that data that you can import into CAD programs. A LAS file format is ideal for storing large amounts of data but isn’t recommended for sharing data with survey clients. This file format is perfect for storing large amounts of raw data, but it’s not the best file type for sharing the data with clients or other parties.
LAZ File Format
A LAZ file format is another form of raw LiDAR data storage. It’s similar to a LAS file format as it contains all the raw data collected during a LiDAR survey, but it also includes additional information that you can import into CAD programs. A LAZ file format is ideal for storing large amounts of raw data and sharing it with clients or other parties. This file format is the best file type for storing large amounts of raw data and sharing it with other survey parties.
LiDAR data is important for any survey project, and the file format you choose will affect the ultimate results. It’s important to select a file format that is easy to store and share with survey parties. You’ll also want to choose a file format that is easy to manipulate and organize for efficient data analysis. When choosing a file format for your LiDAR data, it’s important to consider its storage capacity and its ease of navigation.
RAW LiDAR is simply raw LiDAR data collected with no post-processing applied to it. This is the original form of LiDAR data collected by the sensor and is the most basic format available. It’s an extremely large data file that contains all the laser pulses returned by the surrounding landscape. RAW LiDAR can be difficult and time-consuming to process, and it’s challenging to extract any sort of thematic information from it, such as ground elevations. Because of this factor, most surveyors prefer other LiDAR file formats that are more specific and easier to work with.
Despite being less useful than other types of LiDAR data, RAW LiDAR is still a precious asset. It contains the most accurate data possible and can be used as a benchmark to compare other LiDAR data. It’s often used as a reference point to determine the accuracy of other LiDAR file formats and the accuracy of other data sources in the survey project.
GeoTIFF LiDAR is LiDAR data converted into a TIFF file format. Businesses often use this file format for collecting LiDAR data with terrestrial laser scanners and is less often used with airborne LiDAR data. GeoTIFF is one of the most common LiDAR file formats and has become the standard format for LiDAR data in virtually every industry. This reason is because GeoTIFF is an extremely versatile and user-friendly file format that allows for a wide range of different customizations. You can save GeoTIFF files in either 16-bit or 32-bit color depth, depending on the user’s preferences. GeoTIFF files can store any type of thematic information related to the data, including ground elevations, scan lines, individual points and much more. This feature makes it a very useful and flexible file format for a variety of different surveying projects.
eDX FAS XML LiDAR
eDX FAS xml LiDAR is LiDAR data converted into an FAS XML file format. You would use this file format for collecting airborne LiDAR data. When used for airborne surveys, the system does not store LiDAR data within the FAS file but converts it into that file. The FAS XML file is actually for storing information about the flight, including points of interest, ground control points, flight paths and more. eDX FAS XML LiDAR is one of the most useful and versatile LiDAR file formats available. You can use the FAS XML file to store much different thematic information related to the LiDAR data, including ground elevations, individual points and more. Also, you can link the FAS XML file format to a wide variety of GIS software programs, making it highly useful in post-processing projects.
ESRI Line-based LiDAR
ESRI line-based LiDAR is LiDAR data converted into an ESRI line-based format. Businesses use this file format for collecting LiDAR data with terrestrial laser scanners. While you can use ESRI line-based LiDAR for collecting a wide variety of data, it’s most often used for collecting ground control points (GCPs) and other topographical data. ESRI line-based LiDAR is an extremely useful and adaptable LiDAR file format. You can use the ESRI file to store a wide array of data related to the LiDAR data, including ground elevations, individual points and more. This feature makes it a very applicable and versatile file format for a variety of surveying projects.
Point Cloud XML LiDAR
Point Cloud XML LiDAR is LiDAR data converted into an XML file format. You would use this file format for collecting airborne LiDAR data. Point Cloud XML is one of the most commonly used LiDAR file formats and is especially common among commercial LiDAR data providers. Point Cloud XML is a highly useful and versatile LiDAR file format. You can use the XML to store a wide variety of data types related to the LiDAR data, including ground elevations, individual points and more. This highlight makes it a very helpful and adaptable file format for a variety of surveying projects.
Keyhole Markup Language (KML) LiDAR
KML LiDAR is LiDAR data converted into a KML file format. Businesses often use this file format for collecting airborne LiDAR data. KML is one of the most commonly used LiDAR file formats and is used to store all sorts of different data related to the LiDAR data, including ground elevations, individual points and more. KML LiDAR is an extremely useful and adaptable LiDAR file format. You can use the KML file to store a wide variety of data types related to the LiDAR data, making it a highly applicable and versatile file format for a variety of surveying projects.
07. Exploring 3D LiDAR Data
Since LiDAR involves capturing and storing 3D geometry, it makes sense that we’d see some examples of LiDAR datasets as 3D data.
With so many formats of data available to us these days, it’s nice to find another kind that’s useful for creating virtual spaces. As designers and engineers get more comfortable with the potential of immersive content, we’re seeing new ways to use these kinds of datasets.
A point cloud is basically a collection of points representing surface features detected by a sensor such as LiDAR or radar. It’s a mesh comprised of vertices connected by edges; each vertex has its own location in space as well as other attributes such as color, texture coordinates, etc., depending on what type it represents (i.e., rock vs. tree vs. manmade structure).
3D Data Structure
All 3D LiDAR data is structured in the same way. The dataset comprises points, lines and polygons. The system stores these individual features in a simple text format that any platform or software application can read easily.
Each line represents a point in space, with each point described using three values: XYZ. The X, Y coordinate pairs show the X-axis and Y-axis coordinates of the object’s position on the Earth’s surface and an additional Z value which represents the height above sea level.
Lines also have properties, such as attribute information about what makes it unique and any associated metadata for further clarification regarding its purpose within your project scope. For example, “this line represents a wall.”
Polygons are simply collections of points connected to form shapes, such as circles or squares; they can also represent complex surfaces such as parking lots where multiple adjacent lines intersect at one location to form one area polygonal shape representing that region on the Earth’s surface.
A point cloud is a set of points in 3D space. One or more XYZ coordinates, plus an optional label, may represent each point. You can store points as a list, matrix, table or set of values. Also, you can store the points in files or databases.
A mesh is a collection of 3D points, each with its own coordinate in space. You can use meshes in several applications:
Other forms of visualization.
3D Point Cloud and Digital Elevation Model
A 3D point cloud is a collection of measurements of the distance between the LiDAR unit and various points on the ground. You can use it to create a digital terrain model, which is basically a 3D model of the surface. In the case of terrestrial LiDAR systems, you would use the point cloud to form a digital elevation model (DEM). The DEM is basically a model of the ground’s surface with accurate measurements of its height. With aerial LiDAR systems, you use the point cloud to make a digital surface model (DSM). In that case, the DSM is basically a 3D model of the ground’s surface. You can use this model to identify the type of ground. Also, you can use it to measure the exact distance between two points.
LiDAR Is an Amazing Tool to Observe the World
LiDAR is an amazing tool to observe the world, and many industries use it, from archaeology to geology. You can collect LiDAR data using airplanes, helicopters or drones — the latter being a popular choice due to their ability to fly for long periods at low altitudes.
When you hear someone mention “LiDAR,” they’re usually referring specifically to airborne laser scanning (ALS). This process involves programming a drone with flight paths that allow it to map out an area in three dimensions while collecting data on things such as vegetation height and density, water depth and velocity of flowing streams, rivers or creeks.
For instance, researchers and farmers can use the resulting 3D model to compile detailed information about land management practices, such as irrigation systems or crop rotation patterns.
The data structure involved in LiDAR point clouds is not that complicated, and that’s why we chose this domain to explore what it means to store structured data.
08. How to Process LiDAR Data
The use of LiDAR technology is increasing day by day because of its highly accurate results. The most important step for creating a map from LiDAR data is the processing of raw data. There are many tools and software packages available to process LiDAR based on the data type, geographical location and accuracy required in the final output.
LiDAR data is a 3D representation of the real world captured by a laser scanner. This type of data has many applications in different industries, ranging from urban planning to archaeology to forestry. The various LiDAR scans are classified based on the flight height and the laser scanner’s scanning angle, such as Airborne LiDAR and Terrestrial Ground Scan.
Importing LiDAR Data
When you receive your LiDAR data, you’ll need to import it into your surveying project. You can do this manually or automatically. Importing data manually is best when working with smaller data files and when you know that the file format you received won’t change. When manually importing data, you’ll need to use special software to open the file and then manually input the data into your survey project.
When automatically importing data, you’ll be able to import it directly into your survey project without needing to open and import the file. This option is best when dealing with larger data files that would take too long to manually import or when you are unsure what file format you received and don’t want to risk changing it. Surveying projects can benefit from a wide variety of different LiDAR file formats, so it’s important to know what each format offers. Choosing the right file format for your project will help ensure that your data is high quality and usable, which will lead to higher survey accuracy and better results.
Clean the LiDAR Data
The first step in processing LiDAR data is to clean it. LiDAR data is noisy, and it’s important that you understand the noise in your LiDAR data before you do anything else. There are many types of noise in LiDAR and each one of them can be problematic for your analysis:
Errors – These are the most common error type found in LiDAR datasets, and they can cause problems when processing. The most common error is a single point or polygon that does not line up with other points/polygons around it because of misalignment between scans or regarding elevation data (elevation errors), such as buildings sitting on top of each other.
Inconsistency – This type of noise is similar to errors, but not caused by user input. For example, if there is an enormous gap between scans, then there may be some inconsistencies related to gaps in coverage between those two sets of scans. This may also cause issues such as having vegetation growing through buildings, which causes issues when trying to capture buildings correctly (i.e., capturing a building type incorrectly).
Extract Point Cloud from LiDAR Data
Extracting a point cloud from LiDAR data is one of the most important steps in the data processing pipeline. There are many software packages for extracting points from LiDAR datasets, and each has its own strengths and weaknesses. This section’s goal is to help you select a suitable tool for extracting point clouds from your LiDAR data.
3D Building Model from LiDAR Data
Once you process the LiDAR data, it’s time to build a 3D building model. You can use a 3D building model for many applications, such as creating virtual tours and virtual reality experiences.
Build an accurate 3D Building Model from LiDAR data:
Input 3D point clouds into the software program of your choice (examples include Autodesk ReMake or Civil Viewer)
Use photogrammetry techniques to accurately align photos taken at different angles with one another
2D Image Processing from LiDAR Data
In this section, you will learn how to process LiDAR data to create Normalized Difference Vegetation Index (NDVI) and Red-Green-Blue (RGB) maps. The first step is to import the point cloud into your computer as a terrain dataset. Terrain datasets are an image of each LiDAR point with a height value associated with it at that location on the ground.
Once you import it into your computer, you can then perform analysis on it using specialized software called rasterization tools, such as ENVI or ER Mapper Pro. After processing in these programs, they will produce either an NDVI or RGB image for further analysis or display purposes.
There are many challenges involved in 2D image processing from LiDAR data, such as speed limitations when working with large datasets and inconsistencies between different sensors used for collecting LIDARs, which may cause errors during subsequent analyses of those images if you do not consider these factors properly beforehand by those performing them (e.g., using wrong units).
However, there are ways around these issues, such as using new methods like machine learning algorithms to help automate some steps involved without needing human interaction. This factor makes it possible for less experienced users to gather quick results without spending too much time doing this kind of work manually.
Create NDVI and RGB Maps
Once you have a set of down-sampled LiDAR data and a rasterized version of your map, you can use the NDVI and RGB tools to create maps that show vegetation health. You calculate the Normalized Difference Vegetation Index (NDVI) by comparing the amount of green vegetation in an area with its surroundings. The higher the NDVI value, the more healthy or productive your plant life will be.
The RGB tool allows users to create color space based on any three points on a spectrum for them to visualize different data such as temperature or elevation. If you want something really simple, try using red for high values and blue for low values — but don’t limit yourself. You could also choose different colors based on how much distance there is between two points in your map: If one point represents mountains while another represents valleys, then maybe yellow would be the best choice, so it does not blend too much when you view it from above to see both areas at once without changing your perspective too much.
LiDAR data is useful for 3D and 2D mapping, but it’s not always easy to turn those raw LiDAR points into something you can use. By processing LiDAR data properly, you can create products such as point clouds, DEMs and DSMs with the right software.
09. How to Get the Most Out of LiDAR Data
You can use LiDAR data for a wide range of applications, including surveying and mapping, disaster recovery, industrial inspection and autonomous vehicle navigation. Here are some tips for getting the most out of LiDAR data:
Acquire More Data Than You Need
When it comes to LiDAR, you’re better off having more than you need. Yes, this will cost more money up front and take more time to gain the data, but this is a good thing. After all:
You can always throw out data later if you don’t need.
You can always merge data together later if needed.
You can split parts of your dataset into separate ones as needed.
And finally, once again, because we are talking about LiDAR — and given how much additional processing power the technology requires — you can reprocess your raw point clouds before importing them into CAD models or other software programs that need elevation data.
Collect Quality Data
You should first ensure that you are acquiring quality data. LiDAR data is expensive, so you want to get as much as possible out of the investment. While it might be tempting to purchase cheap LiDAR, this can lead to errors in your data and make the rest of your workflow more difficult than it needs to be.
Acquiring high-quality LiDAR is especially important if you’re using it for asset management purposes — if something is inaccurate, then it doesn’t matter how much time or money you spend on analysis because it won’t produce useful results.
LiDAR scanners aren’t perfect, but there are ways around these inaccuracies depending on what kind of information you need to collect.
Pay Attention to Quality Control
Quality control is important in any field, and it’s no less so for LiDAR data. When you’re gathering information from the Earth’s surface, it’s best to make sure it’s reliable and accurate.
You can do this through a few simple steps:
Check your data before you leave the field. Make sure you test all of the equipment before setting off on an expedition; this includes checking flight plans and making sure that everything is ready to go (including yourself).
Keep a logbook with all of your findings throughout the day or week that you’ve been working with LiDAR data collection. This will help keep track of potential problems that may arise during processing later on. If something goes wrong during processing time, looking back through previous days’ work could help identify what went wrong to fix those errors quickly, so they don’t affect future flights over areas where they’ve already flown recently.
Choose the Right Point Density
If you’re working with LiDAR data, there’s a good chance you’ll want to spend some time thinking about the point density. The number of points per square meter (PPSM) is the most objective measure for assessing the quality of LiDAR data. It’s a function of three factors: the minimum distance between points (microns), maximum distance between points (microns) and object size in meters squared. For example, if we are flying at 100 meters above ground level and our maximum distance between points is 25 meters and our minimum distance between points is 2 meters, then we have a PPSM value of 400.
Planning is key to getting the most out of your LiDAR data and can save you time and money. It helps you get the most out of your project, preventing you from having to repeat work or redo it later. It also keeps costs down by avoiding unnecessary purchases that you could have avoided with some forethought and planning.
Planning will help ensure that your LiDAR data gets used as effectively as possible — and that means more return on investment for all involved in the process.
There’s No Such Thing as Too Much Data
With more data, you have more information — more options, flexibility and creativity. Think about it: Does every option need to be an exact duplicate of the same thing? Or can you use some of your other data sources to complement your LiDAR data?
What’s more important is not whether you have enough LiDAR data; what matters most is that you understand the value of having enough LiDAR points in each area.
10. Top 4 LiDAR Data Sources
LiDAR data is available in a number of public and private databases. Here are four of the best sources to look for LiDAR data:
Government Data: Some governments have been collecting LiDAR data for years. The most notable example is the U.S. government’s National Elevation Dataset (NED), which contains elevation data from more than 90% of the U.S. coastline. Other governments have smaller datasets that may not be as comprehensive but can still be useful.
Commercial Data: Commercial sources are a great place to find LiDAR data. Businesses that use LiDAR for surveying or mapping purposes often sell their data to third parties. This can include companies that specialize in real estate, natural resources and construction.
Academic Data: Universities and research labs also collect LiDAR data, especially in areas such as meteorology and geology.
Crowdsourced Data: Finally, there is also a growing number of crowdsourced LiDAR databases that anyone can access. These databases often contain lower-quality data, but they can still be useful if you need to quickly find a dataset with certain features.