to capture detailed geographical and structural data, which is used for the planning, development, and maintenance of transportation infrastructure (Olsen et al. 2012).

Photodetector and Receiver Electronics

Once the laser light is reflected back from the target, it is captured by the photodetector. This component is sensitive to the specific wavelengths of light that the Lidar system uses. The receiver electronics then convert the light signals into digital data that can be processed for various applications. The sensitivity and accuracy of the photodetector are paramount, as they directly impact the precision of the Lidar measurements. Photodetectors in Lidar systems must accurately capture returning light pulses even at high speeds and over long distances, which is significant for transportation applications where vehicles and roadways are involved. Advanced Lidar systems utilize photodetectors that can discriminate between different signal wavelengths and intensities (Shan 2018). This capability enhances the system’s ability to detect subtle differences in the return signals, which supports detailed surface and feature recognition on complex transportation infrastructures, such as bridges and tunnels. For instance, the integration of dual-wavelength photodetectors has been shown to significantly improve road feature recognition by distinguishing between pavement and lane markers more effectively (Behroozpour et al. 2017).

The efficiency of these photodetectors is further augmented by state-of-the-art receiver electronics, which are designed to rapidly process large volumes of data. These electronics convert the analog signals received from the photodetector into digital form, a process that must occur with minimal error to maintain data integrity. The digital conversion involves filtering out noise and amplifying the signal to ensure that the data used in creating digital elevation models (DEM) is highly accurate and reliable. The calibration of the receiver electronics also plays a significant role in ensuring that the system maintains its accuracy over time. Regular calibration against known standards helps to mitigate any drift in measurements and maintains the system’s reliability, especially in dynamic environments typical of transportation applications (Lichti 2010). The combination of advanced photodetectors and sophisticated receiver electronics ensures that Lidar systems can provide the high-resolution, precise data needed for everything from highway planning to real-time traffic management.

Measurement Modes

There are also several measurement determination modes by which scanners operate. Note that some of these can be used in combination:

• Time of flight: calculates the distance to the object by measuring the round-trip travel time between the outgoing and incoming pulse (Shan 2018). These types of scanners are the most common system and are incorporated into diverse platforms and several of the measurement modes in this list.
• Discrete pulse: provides distinct points based on multiple returns off different intersecting surfaces for each individual emitted laser pulse. It detects peaks in the return waveform when a portion of the light hits different objects across the footprint. For example, as a Lidar pulse penetrates a tree canopy, multiple returns can be obtained on different branches in the tree as well as the ground surface below provided there is sufficient opening for the light to penetrate (Shan 2018).
• Full waveform: digitizes the entire waveform such that advanced filters can be applied to analyze the return pulse to support tasks such as classification (Mallet 2009).
• Phase modulation: evaluates the distance traveled by the pulse based on differences in the phase of the outgoing and incoming pulse. These systems were initially faster than the time of flight; however, that is no longer the case. They often have limited range capabilities (Shan 2018).

digital models of the scanned area. These tasks require the deployment of advanced algorithms and substantial computing power, which are necessary to handle the massive volumes of data produced during Lidar surveys (Fernandez-Diaz et al. 2007). To manage these data effectively, modern Lidar systems incorporate complex data processing technologies that often use Machine Learning (ML) and Artificial Intelligence (AI) to enhance data accuracy and utility (Mirzaie 2022).

Moreover, the ability to process Lidar data in real-time or near-real-time is becoming increasingly important. Real-time processing enables immediate decision-making based on the most current data available, especially for dynamic environments such as active traffic management and emergency response scenarios. For instance, in traffic management, real-time Lidar data processing can help in monitoring traffic flows and detecting incidents on the roads swiftly, facilitating quicker responses to traffic conditions (Li and Ibanez-Guzman 2020).

Additionally, Lidar data processing units are equipped to integrate data from several sources, enhancing the Lidar data with additional contextual information and attribution. For example, integrating GNSS data can improve the geolocation accuracy of the Lidar scans, while data from other sensors, such as radar and cameras, can be used to enrich the Lidar data, providing a more comprehensive view of the environment (Yuan et al. 2022). This process supports a variety of applications, from urban planning and landscape assessment to environmental monitoring and disaster management.

Software and User Interface

The software component of a Lidar system is required to transform raw data into actionable insights. It facilitates the processing, analysis, and management of the data captured, enabling users to manipulate, visualize, and derive meaningful interpretations from the Lidar measurements. The user interface plays a pivotal role in determining how effectively operators can interact with the system. It aids in configuring the system for specific tasks, managing data flow, and interpreting complex datasets, which support decision-making processes in transportation planning and management (Olsen et al. 2013).

Modern Lidar software incorporates advanced features, such as ML algorithms, which automate many data processing tasks. These features enhance the detail and accuracy of the models created, making Lidar an invaluable tool in urban planning, roadway design, and traffic management. For instance, ML can be used to differentiate between vehicular and pedestrian traffic in urban Lidar datasets, supporting city planning (Mirzaei 2022) efforts for improved safety and mobility. Additionally, the integration capabilities of Lidar software allow for its use with other GIS technologies, enhancing its application in environmental monitoring and infrastructure management. This integration supports projects that require precise georeferencing, such as mapping flood risks or planning new transportation routes (Olsen et al. 2024).

The software also includes sophisticated algorithms capable of filtering noise or unwanted objects and improving the accuracy of the data. These algorithms are important in environments where Lidar must operate under less-than-ideal conditions, such as in poor weather or high-traffic areas (Mirzaei 2022). Furthermore, the advancements in real-time data processing and visualization capabilities of Lidar software have revolutionized how traffic flows are monitored and managed. These technologies allow for immediate adjustments to traffic signals and notifications to drivers, reducing congestion and improving road safety.

Each component of the Lidar system is important to its functionality, enhancing the technology’s capacity to deliver precise and detailed 3D representations of physical environments. Comprehending these components and their interaction can help maximize the efficiency of Lidar systems and broaden their use across transportation and beyond. As Lidar technology advances,

the development of these components is expected to progress, further augmenting the capabilities of this important technology in supporting diverse applications such as transportation, environmental monitoring, and infrastructure management.

Lidar Platforms

Lidar sensors can be effectively mounted on a variety of platforms (Figure 1), each selected based on the specific requirements and scope of the intended application. The range of platforms includes fixed-wing and helicopter airborne systems, uncrewed aerial systems (UAS), vehicle-mounted units, backpack configurations, handheld devices, robotic systems, pocket-sized instruments, and others. The selection of a suitable platform typically depends on several criteria such as the area size, the detail required, accessibility of the target location, and budget constraints (Wang and Menenti 2021). Each platform brings its own set of advantages and challenges, influencing operational efficiency and the quality of the data collected. Most platforms co-acquire photographic imagery with the point cloud data. It is also important to understand that there can be a wide range of data quality within a platform. For example, mobile Lidar systems range from inexpensive asset management grade systems (dm-level accuracy and resolution) to surveying grade systems (cm-level accuracy and resolution).

hour (JOUAV 2024). This makes them especially suitable for environmental monitoring and large-scale topographic surveys. On the other hand, rotary-wing Lidar platforms, such as those mounted on helicopters, excel in linear environments that demand detailed data collection at lower speeds and altitudes. They are ideal for complex urban landscapes or rugged terrains where precision is key, supporting detailed infrastructure inspections and urban planning.

In the realm of transportation, airborne Lidar is increasingly utilized for the planning and monitoring of infrastructure projects, such as highways, railways, and bridges. The high-resolution data (1–100 points per square meter typically) obtained from these systems can provide useful data in areas prone to natural disasters such as flood zones or earthquake-prone regions (Seydi et al. 2019). The primary drawback of airborne Lidar systems is the relatively high cost of operation. This includes the expenses related to flight operations, maintenance of aircraft, and the sophisticated data processing required to interpret the vast amounts of data collected. Additionally, these operations require skilled personnel not only for flying the aircraft but also for managing the complicated sensors and processing the data, further adding to the cost (Lohani and Ghosh 2017). Nevertheless, costs have decreased substantially as the technology capabilities have evolved and become more accessible, resulting in more streamlined acquisition and processing workflows. In many cases, multiple organizations pool funds and share the data to keep costs down.

The operation of airborne Lidar systems is also contingent upon weather conditions and adherence to strict airspace regulations, which can limit their operational readiness and flexibility. However, the advancements in sensor technology and more efficient data processing methods continue to enhance the effectiveness of airborne Lidar, reducing operational costs and expanding its applicability across various sectors, particularly in transportation. These systems are integral to the quick assessment and management of existing transportation infrastructures, offering detailed data that supports the maintenance and improvement of safe and efficient transport networks.

Uncrewed Aerial Systems

UAS, or drones equipped with Lidar sensors, present a flexible and cost-effective alternative to traditional airborne Lidar platforms. UAS are particularly effective for bridge inspections and detailed topographic data collection over smaller or complex terrains that are difficult to access with larger aircraft or boots-on-the-ground personnel. Their growing use in sectors such as agriculture for crop monitoring and construction for site inspections underscores their utility in environments where precision and frequency of data collection are dominant. Mallela et al. (2018) conducted a detailed study on the benefits and challenges of UAS technology in transportation construction.

The flexibility of UAS comes from their ability to quickly deploy and operate at lower altitudes, enabling the capture of finer details that are necessary for accurate modeling and analysis in transportation projects. For instance, UAS are used to survey road construction sites, inspect bridges, and monitor traffic patterns, providing valuable data that helps in the maintenance and development of transportation infrastructures (Gupta et al. 2021). Their relatively low operational costs and the ability to maneuver in tight spaces make them an excellent choice for ongoing construction monitoring and urban planning, where frequent updates on progress and conditions are necessary.

However, while UAS offer several advantages, they are not without limitations. One significant constraint is battery life, which can restrict flight duration and thus the area that can be surveyed in a single session. This limitation often requires careful planning to maximize efficiency during data collection missions. Additionally, the operation of UAS is subject to stringent regulations that vary by region, which can impact their flexibility and usability in certain contexts. For example, flight restrictions in populated or sensitive areas can limit the use of drones, affecting their deployment in urban transportation studies where such data might be most needed. The Federal Aviation

Administration allows UAS usage for pilots with a Part 107 license which has restrictions of visual line of sight and operation within 400 feet of the ground. In the coming years, an updated provision, Part 108, will be released, which may relax some of these requirements. Note that local or other federal agency restrictions and DOT policies must be followed in addition to the FAA Part 107 requirements when operating in or near public spaces, national parks, airports, roadways, or military bases. The FAA and FHWA provide a fact sheet (FAA and FHWA 2023), which provides guidance to state and local agencies on legal authority for developing regulations for UAS usage.

Moreover, regardless of their smaller size and agility, UAS can sometimes struggle with payload limitations. The weight and size of high-quality Lidar sensors can restrict the types of drones that can carry them, potentially impacting the quality of data collected. Advances in technology are gradually overcoming these challenges by miniaturizing sensors without compromising their capability, thus broadening the applications of UAS in detailed asset management and transportation analysis.

In the transportation field, the use of UAS technology to collect Lidar data is expanding rapidly given their ability to efficiently collect data over project sites. UAS are proving indispensable for real-time traffic monitoring and incident response, where they can quickly provide important data to traffic managers and response teams. UAS is also increasingly used in disaster response scenarios within transportation networks to assess damage and plan effective rerouting strategies. With current trends in technology innovation reducing the limitations of battery life and sensor size, UAS equipped with Lidar sensors will likely become increasingly used to support many more applications for efficient maintenance and operation of transportation infrastructure worldwide.

Robotic Lidar

Robotic Lidar systems represent a significant advancement in the integration of Lidar technology with autonomous navigation capabilities, making them especially suited for environments where continuous and precise monitoring is needed. These systems are increasingly deployed in sectors such as mining, warehousing, and even in large-scale industrial facilities, where they facilitate important tasks including equipment tracking, inventory management, safety inspections, and comprehensive operational planning (Biber and Fleck 2009).

The core advantage of robotic Lidar systems lies in their ability to operate autonomously, navigating and scanning environments without the need for direct human intervention. This capability is particularly valuable in unsafe areas, such as active mine sites or chemical plants, where human access should be minimized to reduce the risk of injury. Robotic systems can continuously monitor structural integrity, track changes in the environment, and even detect early signs of equipment failure, contributing significantly to workplace safety and operational efficiency. In addition to these safety enhancements, robotic Lidar systems offer the benefit of providing highly consistent and repeatable data. Unlike manual survey methods, which can vary depending on the operator, robotic systems follow predefined paths and parameters to ensure that each scan is performed identically. This consistency is important for applications such as longitudinal studies where changes in the environment or infrastructure need to be accurately measured over time. Furthermore, in warehousing and logistics, robotic Lidar systems are utilized to optimize the layout and operations of facilities. They help in creating highly accurate 3D maps that can be used to plan more efficient storage solutions and improve the flow of goods (Biber and Fleck 2009). Automated guided vehicles equipped with Lidar not only navigate safely around a warehouse but also contribute to better inventory management by identifying and tracking items as they move throughout the space (De Ryck et al. 2020).

Robotic Lidar systems do face challenges. The initial setup, training, and maintenance costs can be high, reflecting the complexity of the technology. Additionally, the integration of these

systems into existing operations often requires significant planning and adaptation of current workflows. There is also the challenge of ensuring that the robotic systems can adapt to dynamic environments where unexpected obstacles or changes might occur, which requires sophisticated programming and sensor integration.

As technological advancements continue, however, the capabilities of robotic Lidar systems are rapidly evolving. Improvements in AI and ML are enhancing the ability of these systems to operate in increasingly complex environments with greater autonomy and adaptability. This progress is expected to further expand the applications of robotic Lidar systems by enhancing precision, safety, and efficiency in their operations.

Vehicle-Mounted Lidar

Vehicle-mounted Lidar systems, a main component of mobile mapping technologies, are indispensable for comprehensive transportation and asset management (Olsen et al. 2013). These systems are particularly effective in capturing detailed, street-level data (Che et al. 2021), supporting a range of applications from transportation planning to infrastructure maintenance and safety analysis.

Mounted on various vehicles, including cars and potentially trains, these Lidar systems facilitate extensive data collection while on the move, making them ideal for documenting vast stretches of roadways and railways. This capability is particularly beneficial for road surface inspection, where continuous, detailed monitoring of pavement conditions is required for safety and efficiency. The integration of Lidar systems on trains could revolutionize how rail infrastructure is maintained, allowing for near real-time detection of track anomalies and wear, thus significantly enhancing predictive maintenance strategies.

In urban environments, vehicle-mounted Lidar systems support intelligent transportation systems and smart city applications. They excel in gathering high-resolution information on road signs, lane markers, and road surface conditions, which are integral to the operation of autonomous vehicles and advanced driver-assistance systems. This detailed data supports the precise navigation capabilities required by self-driving cars, with promises of safer, more efficient urban traffic management (Li and Ibanez-Guzman 2020).

However, the deployment of vehicle-mounted Lidar involves substantial challenges, particularly regarding the high volume and speed of data collection. These systems generate large datasets that necessitate advanced processing capabilities and robust storage solutions to manage the rapid accumulation of detailed spatial information. The complexity of this data also requires sophisticated algorithms capable of quickly filtering and analyzing the information to derive actionable insights, ensuring that the data’s full value is realized (Che and Olsen 2019). Additionally, the physical integration of Lidar sensors on vehicles demands that these systems be durable enough to endure road and rail conditions, including vibrations and varying weather elements. Maintaining system calibration and alignment is essential to ensure the accuracy of the data collected, adding to the operational challenges of using vehicle-mounted Lidar systems. The advantages of vehicle-mounted Lidar for mobile mapping in transportation are significant. They provide an unparalleled level of detail and coverage, enhancing road safety and efficiency studies. As sensor technology and data processing techniques continue to evolve, the limitations currently faced are expected to diminish, further solidifying the role of vehicle-mounted Lidar in future transportation systems.

Looking ahead, the potential for vehicle-mounted Lidar systems extends beyond roads to railways and other forms of public transport, where they could provide continuous, automated inspection and monitoring capabilities. This routine “checkup” could not only improve the reliability and safety of these transport systems but also optimize maintenance workflows and reduce

downtime because of repairs. The integration of these systems with upcoming technologies such as 5G and AI is poised to transform how transportation infrastructures are monitored, managed, and developed, particularly with developing HD maps to support autonomous vehicles. The expansion of vehicle-mounted Lidar into new areas of mobile mapping presents potential opportunities for creating more connected and intelligent transportation networks across the globe.

Backpack Lidar

Backpack Lidar systems, known for their portability and ease of use, are perfectly suited for pedestrian-scale mapping projects that require mobility and flexibility such as mapping sidewalks or curbs. These systems are especially valuable in environments that are inaccessible to larger, vehicle-mounted or airborne systems, such as densely populated urban areas, narrow historical alleyways, or rugged hiking trails (Erdal 2021).

The compact nature of backpack Lidar allows operators to comfortably carry and operate the system while walking, enabling the collection of data from a human perspective. This capability is advantageous for projects focusing on detailed and precise mapping of small or crowded spaces, such as historical site documentation, urban landscape studies, and cultural heritage conservation. For instance, in historic centers where the preservation of the original architecture is of interest, backpack Lidar can provide detailed 3D scans that help maintain the structural integrity and authenticity of these sites.

In addition to cultural heritage applications, backpack Lidar is instrumental in environmental science, particularly in studying and documenting trails and natural habitats that are not easily reachable with traditional Lidar setups. These systems are designed to operate in a variety of terrains and under different weather conditions, offering robust and reliable data collection capabilities (Beland et al. 2019). However, while backpack Lidar systems offer substantial flexibility and access, they do come with limitations. The primary challenge is the physical burden placed on the operator, as carrying the system for long durations can be physically demanding. Moreover, the quality of the data collected can sometimes be influenced by the operator’s movement, which may introduce noise into the data that requires sophisticated processing algorithms to filter. Another limitation is the range and resolution of data collected, which may not match the higher capabilities of more substantial Lidar systems. The benefits of using backpack Lidar in appropriate settings—particularly for detailed, ground-level environmental and architectural studies—are significant. These systems provide a unique dataset that is invaluable for applications requiring detailed, human-scale perspectives and granular environmental understanding.

Given current trends in reduced weight and improved efficiency of backpack Lidar systems, these systems will likely become more versatile and easier to use across a broader range of applications. As such, backpack Lidar systems are especially important for projects where accessibility and detailed close-range data collection are paramount.

Handheld Lidar

Handheld Lidar devices have significantly enhanced the accessibility and versatility of Lidar technology, making high-precision spatial data collection convenient and straightforward. These compact, easy-to-operate systems can be deployed rapidly, which is ideal for a variety of applications where speed and ease of use are paramount (Chen 2023).

The utility of handheld Lidar is particularly notable in settings that demand mobility and where space is constrained. For indoor mapping projects, such as within buildings or other enclosed structures, handheld Lidar provides data that helps architects, engineers, real estate developers, and insurance adjusters understand and document spatial environments efficiently. Similarly,

in the realm of law enforcement and forensic science, these devices offer the ability to quickly and accurately document and reconstruct crime scenes for later analysis. Additionally, in the cultural heritage sector, handheld Lidar allows for the detailed documentation of artifacts and historical sites without the need for intrusive setups that could potentially damage sensitive materials.

Handheld Lidar systems do have limitations, primarily in terms of data resolution. While they are highly effective for many applications, the quality of data captured with handheld devices generally does not match that of larger, more robust systems such as airborne or vehicle-mounted Lidar. This limitation is primarily because of the smaller size and lower power of the sensors used, which impacts their ability to capture data at very high resolutions over larger distances. Moreover, while the portability of handheld Lidar systems offers unmatched convenience, it can also introduce variability in data quality, dependent on the operator’s skill and experience. The manual operation of these devices can lead to inconsistencies in data collection, which may require additional processing or verification to ensure accuracy.

The benefits of handheld Lidar, especially in terms of cost, ease of use, and accessibility, often outweigh the drawbacks (Chen 2023). Ongoing advancements in sensor technology and data processing are likely to further enhance the capabilities of handheld Lidar systems, reducing limitations related to data quality and operational consistency. As technology progresses, these devices are expected to become even more integral to many industries, expanding their utility beyond their current applications and making Lidar technology accessible to a broader audience.

Pocket Lidar

Pocket Lidar systems, the latest innovation in Lidar technology, exemplify the trend toward miniaturization and accessibility in high-tech tools given rapid advancements in sensor technology and battery efficiency. They can be considered a subset of handheld Lidar systems. This technology is currently implemented on iPhone Pro and iPad Pro smart devices. These compact devices are engineered for casual or spur-of-the-moment use, making them highly suitable for personal navigation aids, quick environmental assessments, and urgent applications by inspectors, engineers, maintenance personnel, researchers, and first responders. A key advantage to these systems is the limited training required. Designed to fit within a pocket or a small bag, these devices provide the ability to quickly gather spatial data without the need for cumbersome equipment setups. This immediate data capture capability is invaluable in scenarios where time is of the essence, such as during emergency response operations where understanding the immediate environment is vital for effective decision-making. Pocket Lidar has shown success in obtaining reliable measurements for construction and maintenance applications including stockpile volumes, utility trench mapping, culvert assessment, maintenance hole mapping, and curb ramp Americans with Disabilities Act (ADA) compliance assessments (Olsen et al. 2024–2025 under review). Pocket Lidar can be rapidly deployed to assess accident scenes, disaster-impacted areas, or rapidly changing environments, providing useful data that helps in orchestrating effective responses.

However, the reduced size of pocket Lidar systems does come with certain limitations. The data quality and range (maximum of 5 meters) of these devices are generally inferior to those of their larger counterparts, such as vehicle-mounted or airborne systems. The systems also show substantial drift for areas larger than 10 m × 10 m in size (Olsen et al. 2025 under review). The resolution (cm level) and accuracy (cm level) of the data collected with pocket Lidar are often sufficient for general use but may not meet the stringent requirements needed for some applications in professional-grade surveying or detailed scientific analysis. Results can also vary substantially based on the apps used and the settings within the app. Nevertheless, the benefits of portability and ease of use can make these devices suitable for many applications that are logistically challenging or inefficient with other technologies. They can also be used to supplement data

collected with mobile or terrestrial systems. As technology continues to advance, the capabilities of pocket Lidar will likely expand, further enhancing its utility and expanding Lidar use into even more applications where quick, easy-to-use spatial data collection is required.

Pocket Lidar devices are redefining the landscape of personal and emergency geospatial data collection by offering an unprecedented blend of convenience, accessibility, and immediacy. These attributes make them an exciting addition to the array of tools available to professionals and casual users alike, promising to broaden the reach of Lidar technology to everyday life.

Comparison of Lidar Platforms

Each Lidar platform, as detailed in Table 3, offers distinct advantages and addresses specific challenges, particularly in terms of data quality and suitability for different applications. The choice

Table 3. Summary of the advantages and disadvantages of common Lidar platforms.