Practices for Collecting, Managing, and Using Light Detection and Ranging Data (2025)
Chapter: 6 Case Examples
CHAPTER 6
Case Examples
The survey results presented in Chapter 5 have shown that the integration of Lidar technology in transportation projects has simultaneously been both transformative and challenging for DOTs across the United States. To gain a deeper understanding of the practical applications, benefits, and challenges associated with Lidar technology, in-depth interviews were conducted with representatives from selected state DOTs. Several (15) expressed their willingness to participate in an interview in the questionnaire (see Appendix C for the case example interview questions). From those, a diverse group was selected to ensure a comprehensive representation across different regions of the country (Table 11).
This chapter presents detailed case examples of Oregon, Tennessee, Wisconsin, Colorado, and Texas DOTs, providing insights into their experiences with Lidar technology. Each case example explores the background of Lidar usage, the benefits and challenges faced, data management practices, experience gained, and future plans and initiatives.
Table 11. Summary of selected case examples.
| # | State DOT | Division of Transportation | Lidar Platform (Routine) | Applications (Routine and Regular) |
|---|---|---|---|---|
| 1 | Oregon | Geometronics | Terrestrial, Mobile, UAS | Design, Construction QC, Slope Stability, Hydrological Analysis, Road Safety, Mapping, Asset Management, Vertical Clearance |
| 2 | Tennessee | Maintenance Operations | Airborne, Terrestrial, Mobile, UAS | Design, Bridge Inspections, Slope Stability, Hydrological Analysis, HPMS, Mapping, Asset Management, Emergency Response |
| 3 | Wisconsin | BTS/Photogrammetry | Airborne, Terrestrial | Design, Environmental Analysis, Mapping, Wall Monitoring, Bridge Vertical Clearance |
| 4 | Colorado | Transportation Development | Terrestrial, Mobile, UAS | Design, Slope Stability, Hydrological Analysis, Mapping |
| 5 | Texas | Survey and Utilities | Airborne, Helicopter, Terrestrial, Mobile | Design, Hydrological Analysis |
Oregon DOT
Background
The Oregon Department of Transportation (ODOT) has been a pioneer in integrating Lidar technology into many applications in transportation (Singh 2008). ODOT began utilizing static Lidar in 2002, marking over two decades of leveraging various types of Lidar data for enhanced project outcomes. In 2014, ODOT expanded its Lidar capabilities by incorporating mobile Lidar, which is the department’s most frequent usage of Lidar technology. In 2019, ODOT conducted a pilot project to explore the use of UAS Lidar for landslide assessment (Babbel et al. 2019). Following the Hooskanaden landslide mass movement in 2019 (Alberti et al. 2020), UAS Lidar data was collected and utilized for geologic assessments as well as to provide topographic information to support the design. By 2023, ODOT purchased a UAS Lidar unit, which has proven to be highly effective and has exceeded their expectations in various projects. Currently, almost every ODOT project benefits from the precision and reliability of Lidar technology, underscoring its integral role in the DOT’s operations.
Benefits and Challenges
ODOT has harnessed Lidar technology extensively across a variety of applications, significantly enhancing their transportation projects. One of the primary uses of Lidar is for highway improvement and construction projects, where it provides comprehensive existing condition data from the scoping phase through project design. These data are important for survey base mapping, which ODOT indicated was their most frequent application. The integration of mobile Lidar data has modernized their vertical clearance program (Olsen et al. 2013), replacing outdated measurement systems with highly accurate point cloud captures. These measurements allow for precise assessments of entire structures, ensuring more accurate and reliable estimates of clearances. For example, they allow for a true 3D assessment of clearances under bridges and tunnels and can account for the impact of road grade and cross slope to determine the true clearance.
While the accuracy can vary in heavily vegetated areas, typically resulting in deviations of up to a couple of feet when only using onboard GPS and inertial navigational systems, the survey-grade datasets generally achieve an impressive accuracy of around 0.05 feet. This level of precision is almost always sufficient for the wide range of applications within ODOT, demonstrating the substantial benefits and advancements brought by Lidar technology.
Despite the significant benefits and advancements brought by Lidar technology, ODOT faces several challenges in fully leveraging its potential. One of the primary challenges is the automation of feature extraction from Lidar point clouds. The manual extraction process is labor-intensive, requiring substantial person-hours to mine data, particularly for statewide asset extraction such as guardrails. To address this, ODOT has undertaken several research projects with Oregon State University to develop software tools for automated feature extraction. However, many of these tools are still in development and have not yet been implemented statewide.
Additionally, ODOT has invested in semi-automated extraction software, which improves efficiency but still requires substantial effort. Processing capacity poses another challenge. While individual districts can process and extract features for specific projects, statewide extraction is limited to the headquarters office, which currently lacks the capacity to handle such extensive tasks. This limitation is compounded by personnel constraints, as increasing the workforce is tied to budgetary limitations. Expanding their team would help address these capacity issues, but current budget constraints make it a challenging prospect. Nevertheless, ODOT has conducted studies on the ROI of mobile Lidar (Jahanger et al. 2023) and 3D engineering models (Martin et al. 2020) that have helped leadership recognize its value.
Data Management
At ODOT, Lidar data management is a multifaceted process that involves substantial IT investments and strategic data archiving practices. ODOT has implemented a robust internal data server with a capacity of two petabytes, established 4 years ago. This server facilitates local data storage and is complemented by a backup copy stored in a different state, ensuring data safety and integrity in emergency situations that could disrupt a local server. The cost of maintaining such extensive storage infrastructure is significant. To manage data efficiently, ODOT retains the last 5 years of Lidar data locally on the server while archiving older data to a more cost-effective cloud storage solution, ensuring that no Lidar dataset is deleted.
Sharing Lidar data within ODOT is streamlined through virtual private network access, enabling various offices statewide to access the central Lidar server seamlessly. For external data distribution, ODOT employs the MoveIT transfer tool for packages under a terabyte. For larger data sets exceeding a terabyte, physical mailing of hard drives is used, addressing the challenge of distributing extensive datasets.
Data mining processes at ODOT involve both manual and automated approaches, although automation remains a challenge. The organization has partnered with Oregon State University on several research projects to develop tools for automated feature extraction and change detection. For instance, a notable tool, RAMBO, developed for geological assessments helps analyze rock faces from different periods to detect rockfalls or unstable portions of the slope (Olsen et al. 2023). Additionally, ODOT uses semi-automated software to improve efficiency in data extraction, though full automation is still a work in progress.
Ensuring the interoperability of Lidar data with other geospatial datasets is a fundamental aspect of ODOT’s data management strategy. Efforts are made to enhance metadata integrity, ensuring that coordinate system information is accurately embedded within Lidar files. Custom scripts are utilized to add metadata, when necessary, particularly for older systems that lacked this capability. This meticulous approach to data management supports state and federal reporting requirements, such as MIRE, MMUCC, FARS, HPMS, and BMS, facilitating comprehensive and accurate data usage across different applications and mandates.
Experience Gained
ODOT has developed several protocols and gleaned valuable lessons from their extensive experience with Lidar technology. One of the key strategies has been building a strong business case for the use of Lidar data, particularly highlighting instances of high ROI. For example, a study conducted by Oregon State University in 2017 evaluated the ROI of ODOT’s MLS, revealing a 300% return over 5 years. A notable instance includes a passing zone analysis in 2016, which saved ODOT approximately $250,000 compared to traditional methods. Another example is the RoME tool (Jung et al. 2018), which has been utilized on several projects in ODOT to estimate pavement striping quantities. Savings from the use of the RoME tool on one project exceeded the initial research costs and was recognized with an AASHTO High Value Research Award in 2023.
This significant ROI has likely increased further as more use cases have emerged since that assessment. Such compelling evidence has helped ODOT secure funding for new Lidar systems, demonstrating to upper management the technology’s value and leading to investments in newer mobile Lidar systems, even amid budget constraints.
ODOT’s QA practices are thorough and integral to their Lidar operations. These practices include standardized processes for data collection and processing, ensuring accurate metadata documentation, proper coordinate system integration, and meticulous data organization on servers. ODOT places a strong emphasis on assessing the absolute accuracy of data, particularly when not constrained by survey control. This rigorous approach ensures the reliability and usability of data across various projects and applications.
Key experience gained by ODOT includes the importance of data management to avoid duplicate datasets, which can lead to storage inefficiencies. Effective data management practices ensure that only a single iteration of data is saved long-term. Additionally, ODOT has learned that comprehensive training for end-users can maximize the value of collected data. Ensuring that users are proficient in viewing, editing, and extracting data from Lidar datasets has proven necessary, as collecting extensive datasets is futile if they are not actively used.
Training and professional development are cornerstones of ODOT’s approach to staying at the forefront of Lidar technology. ODOT invests in software capable of handling large point clouds and data manipulation, recognizing that data collection is only part of the equation. ODOT’s engineering automation office publishes short training videos and conducts webinars to share tips and tricks for using software tools. While initially conducted monthly, these webinars have been scaled back to a quarterly schedule because of the workload involved. This approach ensures that staff remain updated on the latest techniques and tools, fostering a culture of continuous learning and adaptation.
Future Plans and Initiatives
ODOT proactively collaborates with academic institutions to advance Lidar technology and its applications. One notable project involved a collaboration with the Oregon Institute of Technology, where ODOT used their aerial Lidar unit to collect data to support a river restoration project. This project aimed to build a fish ladder and assess sediment movement before and after water introduction, highlighting the practical and environmental applications of Lidar. These collaborations not only enhance ODOT’s technical capabilities but also foster innovation through academic partnerships.
As the volume of Lidar data grows with future projects, ODOT is focused on scalability in data collection and analysis. The department is continuously updating their baseline data by collecting mobile Lidar, which helps in maintaining a comprehensive and current dataset. However, this initiative increases the volume of data, necessitating efficient data management solutions. ODOT is also exploring new compression technologies as well as the E57 file format, which could potentially reduce file sizes 25–30%. This compression would significantly ease data storage and transfer challenges.
Future research topics of interest to ODOT include developing more efficient asset extraction techniques and automation. Despite the claims of many companies offering automatic extraction services, these processes often still rely heavily on manual labor. Therefore, ODOT is interested in exploring ML and AI for improved recognition and extraction of assets from Lidar data. Additionally, the use of blue-green Lidar for hydraulic engineering and bridge scour assessment is a promising area of research, given its potential to improve data collection in turbid water conditions.
ODOT is always finding new uses for Lidar technology. For example, during the wildfire recovery efforts in 2020 and 2021, ODOT utilized aerial and mobile Lidar to assess and mitigate damage, saving the state significant resources. This experience underscores the importance of remaining adaptable and open to innovative applications of Lidar technology. ODOT encourages other DOTs to continually explore new uses for Lidar beyond their initial applications, as the technology holds vast potential for diverse and impactful uses.
Highlights
ODOT has been leveraging Lidar technology for over two decades, beginning with static Lidar in 2002 and expanding to mobile and UAS Lidar in recent years. Lidar is now integral to nearly all ODOT projects, enhancing precision and reliability in data collection and analysis. Lidar is
primarily used for creating survey-based maps and vertical clearance measurements, significantly modernizing previously outdated processes and improving accuracy and efficiency. However, a major challenge is automating feature extraction from Lidar data, which currently requires extensive manual effort. Efforts to address this include research collaborations and investments in semi-automated extraction software. Full automation remains a work in progress, with statewide data processing limited by personnel and budget constraints. Nevertheless, the creation of an Engineering Automation division combined with regular collaboration with academic institutions and vendors has helped ODOT significantly expand their capabilities.
Tennessee DOT
Background
The Tennessee Department of Transportation (TDOT) has used Lidar technology extensively since 2009. Initially, the implementation involved static Lidar scans, but its usage quickly expanded across a variety of applications. TDOT has been using Lidar primarily for asset information extraction on Interstate and state highways. This technology has become integral to asset management, providing detailed and reliable data for many departments within the organization.
TDOT has developed unexpected applications of Lidar such as determining billboard locations for compliance and advertising purposes. This innovative use of the Lidar point cloud has proven highly beneficial, allowing precise measurements and aiding in ensuring regulatory compliance.
TDOT’s first experience with Lidar involved terrestrial scanning for rock cut analysis, which provided detailed information that traditional surveys could not capture. Additionally, during a significant flooding event in 2019, TDOT utilized Lidar data to assess landslides and settlements by overlaying photogrammetric data collected by drones with original Lidar data. This approach helped quantify the extent of damage and plan effective responses.
TDOT has equipped their UASs with Lidar to supplement surveys, particularly for topographic, railroad, and airport obstruction surveys. The ability to conduct surveys without requiring physical access to dangerous or restricted areas has enhanced safety and efficiency. TDOT continues to explore innovative uses for Lidar, including monitoring rockfalls and collaborating with research institutions to develop new applications for this technology.
Benefits and Challenges
TDOT has realized substantial benefits from utilizing Lidar technology, particularly in geodetic applications. One of the most significant advantages is the enhanced safety for personnel, as Lidar allows data collection from outside of travel lanes, reducing the need for workers to be on the road. Additionally, the speed and accuracy of data collection using mobile Lidar, which can be performed at standard driving speeds, are notable benefits. This method provides a high resolution and density of data, offering detailed insights that traditional surveys might miss. For example, Lidar data can identify features such as rock separations or scarp formations to support geotechnical assessments.
Lidar’s ability to allow retroactive data extraction has been particularly useful for TDOT. They are exploring the use of Lidar intensity returns to assess the retroreflectivity of signs and pavement markings, which, although not perfectly matching traditional retroreflectivity measures, offer a valuable comparative tool. Additionally, Lidar has been effectively used for landslide and rockfall assessments, especially in areas with heavy vegetation where photogrammetry falls short.
Despite these benefits, TDOT faces significant challenges in leveraging Lidar technology. The primary challenge is the extensive time required for data processing and extraction. Historically,
the department’s structure supported larger field crews and fewer office staff, but Lidar’s efficiency in the field and the time-intensive nature of office processing has prompted a reorganization to bolster office capabilities. TDOT is investing in software solutions for automated extraction to improve efficiency.
Another challenge is the cost associated with drones, which initially hindered their adoption. Although TDOT is now utilizing drones more, communication issues regarding data needs persist. Additionally, many users within TDOT are not familiar with the tools required to interact with Lidar files. To address this, TDOT’s vendor has integrated Lidar information with 360-degree imagery, simplifying data extraction without requiring extensive GIS expertise.
Storage of large Lidar files has also been a hurdle. Initially, files were delivered on external hard drives and stored locally, which complicated sharing and increased server space costs. TDOT has mitigated this by having their vendor host the data on external servers, allowing users to download necessary LAS files via a platform, thus improving data accessibility and reducing storage burdens.
Data Management
TDOT has established a structured approach to manage Lidar data, primarily storing it on local servers. Previously, like many other organizations, they stored data on external hard drives, but they have since transitioned to server-based storage to improve accessibility and backup capabilities. The data is organized by project, and while there is no explicit timeline for how long the data is kept, survey-related data is typically archived for the life of the structure it pertains to, effectively meaning it is retained indefinitely.
In some regions, TDOT has invested in larger, dedicated computers purchased about a decade ago to manage and process the data. These computers store processed data rather than raw data, focusing on keeping the most relevant information readily accessible.
TDOT encourages data sharing across its diverse offices, although the practice is still developing. While there are instances of different offices reaching out to share Lidar data, such as between the survey offices and geotechnical sections, the overall awareness of who has what data and how it can be utilized is still growing. As more personnel become informed about Lidar’s capabilities, the expectation is that data sharing will increase.
Regarding data mining, TDOT faces some challenges because of limited in-house capabilities for processing Lidar data. The geodetic and survey sections primarily handle data processing, often extracting necessary information for other departments. For instance, cross-sections required for slope analysis are derived through collaboration with the survey and design teams. TDOT acknowledges that fully automated data mining and differentiation, such as distinguishing between vegetation and ground surfaces, remains a significant challenge. The department currently relies on vendors for asset extraction, who use their proprietary algorithms and tools to automate some of the data extraction processes. TDOT’s primary role is to ensure the extracted data meets their needs in the required format.
Experience Gained
To make a compelling business case for Lidar, TDOT has highlighted several key benefits, including significant savings in labor and time as well as enhanced safety by reducing the need for personnel to work in challenging areas. Lidar’s ability to quickly collect high-resolution data while keeping staff out of the roadway has proven invaluable. For instance, drone-based Lidar can quickly and safely survey landslides, a task that would be dangerous and time-consuming using traditional survey methods.
TDOT has implemented QA practices to ensure the accuracy and reliability of Lidar data. These include a remote sensing chapter in their survey standards manual, which sets standards for Lidar and requires consultants to submit accuracy reports. Metadata is also provided with the output information, detailing limitations and ensuring users are aware of the data’s accuracy, especially since TDOT often collects data with a mapping grade system rather than a survey grade one.
One of the top experiences gained by TDOT is the importance of controlling data quality through accurate project control. Properly setting control points before scanning ensures useful data is collected. Another lesson involves managing the large data sizes associated with Lidar. TDOT has found it beneficial to allow vendors to store and host the data, enabling easier access to required portions via fast internet connections. This approach has streamlined data management, allowing users to download only the necessary segments for their applications.
TDOT addresses training and professional development needs through vendor-provided training when new equipment is acquired, and by organizing statewide refresher courses for software. Additionally, TDOT has developed internal resources, such as how-to guides on their SharePoint site, to support ongoing learning and problem resolution. For users not directly handling Lidar data, training focuses on using applications that integrate Lidar information and simplified GIS tools for accessing and querying extracted asset data. These efforts ensure that TDOT staff remain proficient in using Lidar technology effectively.
Future Plans and Initiatives
TDOT has engaged in multiple collaborations with academic institutions and research bodies to enhance their Lidar technology applications. Notably, they partnered with the University of Tennessee Chattanooga to develop drone-based inspection processes for retaining walls. Although this project experienced some delays, it highlights TDOT’s commitment to exploring innovative uses of Lidar. TDOT has also worked with industrial vendors to evaluate the use of intensity return data from Lidar for assessing sign retroreflectivity.
To address scalability in Lidar data collection and analysis, TDOT plans to expand in-house Lidar capabilities by equipping all regions with necessary Lidar equipment. However, they face limitations in personnel and resources required for extensive data processing and extraction. Despite these challenges, TDOT is optimistic about integrating more advanced Lidar technologies and techniques in the future, including potential automation and improved data processing methods.
Future research topics that would be beneficial to TDOT include more efficient asset extraction techniques, ML for recognizing different assets, and using Lidar for monitoring landslide and rockslope stability. Additionally, TDOT is exploring the potential of using pocket Lidar technology for construction inspections, which could provide a cost-effective and accessible tool for on-site data collection.
TDOT is also investigating the use of Lidar for detecting pavement conditions and potholes, with the aim of improving maintenance responses and reducing damage claims. These initiatives demonstrate TDOT’s proactive approach to leveraging Lidar technology for several transportation challenges and opportunities.
Highlights
TDOT has been utilizing Lidar technology since 2009, initially incorporating static Lidar scans and progressively integrating mobile and drone-based Lidar for various applications. TDOT has found substantial benefits in using Lidar, particularly in improving safety for staff by reducing the need for on-road work. Challenges faced by TDOT include the significant time
required for data processing and extraction, the need for improved data management systems, and limited personnel and resources for extensive Lidar operations. To address these, TDOT has implemented solutions such as storing data on servers, using software tools for automated extraction, and contracting with vendors for data collection and processing.
In terms of experience gained, TDOT emphasizes the importance of maintaining accurate control over data collection processes, investing in QA measures, and providing training for staff to effectively use Lidar technology. They have documented standards for Lidar in their survey manual and offer various training initiatives to keep staff updated on the latest practices and tools. Looking forward, TDOT is expanding its Lidar capabilities and exploring innovative uses, such as monitoring landslide and rockslope stability, using pocket Lidar for inspections, and detecting pavement conditions and potholes. Collaborations with academic institutions and research bodies have been integral to their initiatives, and future research is focused on enhancing automation and ML for asset extraction. TDOT continues to discover new applications for Lidar, demonstrating its value in improving efficiency, safety, and data accuracy across a variety of transportation projects.
Wisconsin DOT
Background
The Wisconsin Department of Transportation (WisDOT) has a rich history of utilizing Lidar technology, primarily managed by the photogrammetry unit within the Bureau of Transportation Services. Initially focusing on static Lidar projects for urban corridors, WisDOT aimed to obtain highly accurate data for city infrastructure improvements such as curb ramps and gutter corridors. Since 2016, WisDOT has expanded its efforts to include aerial Lidar on numerous projects, integrating color aerial imagery to enhance data collection and processing efficiency. This approach allows them to handle large-scale projects effectively, capturing dense data sets of up to 50–70 points per square meter.
In addition to aerial Lidar, WisDOT has sporadically employed mobile Lidar for specific projects. For example, they have recently used mobile Lidar for pavement crack analysis. These efforts have been complemented by pilot projects using UAS Lidar, although these are less frequent because of higher costs. WisDOT has also adopted innovative practices, including the combination of aerial Lidar with color aerial imagery to accelerate the stereo compilation process, significantly reducing the time required for data collection and processing. This has enabled them to complete more projects both in-house and through external consultants. Another notable innovation is the use of mobile Lidar to measure and identify cracks on concrete pavements, allowing for more efficient maintenance planning.
Benefits and Challenges
WisDOT has seen substantial benefits from using aerial Lidar technology. The primary advantage is its ability to cover large projects efficiently, often requiring a dense Lidar data set of 50 to 75 points per square meter. This capability is particularly valuable for extensive projects needing detailed data across wide areas, not just limited to narrow corridors. The integration of aerial Lidar with color aerial imagery has significantly streamlined the stereo compilation process, reducing manual efforts and improving the accuracy of end products, especially on hard surfaces. This approach has resulted in high accuracy, with hard surface accuracies oftentimes less than a tenth of a foot at 95% confidence. Additionally, the availability of county Lidar data has provided quick access to preliminary surface data for designers and engineers, facilitating hydraulic studies and other analyses.
Despite these benefits, WisDOT faces several challenges in leveraging Lidar technology more widely. One major challenge is the complexity of communicating the capabilities and availability
of Lidar data across the various units and sections within the DOT. This issue is exacerbated by frequent turnover among designers and engineers, leading to a lack of awareness about the available Lidar data and how to request it. Additionally, the time constraints and busy schedules of staff members make it difficult to organize and conduct training sessions or informational presentations to disseminate knowledge about Lidar technology. Although efforts are made to reach out to different regions with presentations and meetings, ensuring that all staff are informed and engaged remains a persistent challenge.
Data Management
WisDOT manages Lidar data by storing it on an internal server, which currently holds about 95 TB of data, with the capacity increasing as new projects are completed. The LAS files and the Lidar-specific data are stored permanently in accordance with their retention plan. Additionally, WisDOT stores county Lidar data on Box for easier access. Recognizing the high costs associated with maintaining server space, the organization plans to transition to a cloud-based method for project-specific Lidar data storage in Fall 2024/Winter 2025.
The sharing of Lidar data within the organization is limited because of the lack of necessary software and computing power among most staff. The photogrammetry unit processes the raw Lidar data into final surfaces and key points before forwarding these reduced datasets to regional survey coordinators, who then distribute them to specific engineers or designers. This processed data is what most users within the organization work with, rather than the Lidar point cloud data.
The process of data mining within WisDOT involves minimal automation. Feature extraction is primarily a manual task performed by compilers. However, the organization uses software to create Lidar tiles, bare earth surfaces, and key points. WisDOT has noted that during 2023, the software utilized by WisDOT has improved its automated techniques, resulting in a quicker and more efficient processing workflow with reduced need for post-processing cleanup.
To ensure interoperability of Lidar data with other geospatial datasets and systems, WisDOT maintains detailed records of the collected data, allowing for the retrieval and extension of project areas as needed. Although the unit managing Lidar data does not directly handle compliance with specific mandates or reporting requirements such as HPMS, they ensure that the data can be accessed and utilized by the relevant departments within the DOT for such purposes.
Experience Gained
To effectively make the business case for the use of Lidar data, WisDOT has found that bundling spring collections of both imagery and Lidar for multiple projects has led to significant cost savings. By combining these efforts, they have managed to reduce costs to approximately $400 per flight mile, significantly cheaper than collecting them separately. This practice has enabled WisDOT to collect a substantial amount of data at a relatively low cost.
For QA, WisDOT follows a rigorous process. Consultants initially QC the collected data against provided control points, which are then sent to WisDOT for further validation. The data are tiled into 2,000-foot by 2,000-foot sections and rechecked against control points and independent validation points. This multi-layered QC process ensures high accuracy, with results typically showing 0.06–0.09 feet accuracy on hard surfaces at 95% confidence when using static terrestrial laser scanning. However, this accuracy is a bit higher with control that has been surveyed through GPS methods.
WisDOT has learned several key lessons from their Lidar adoption. For example, having a documented process in place before handling and processing data is important. This preparation allows for a smooth workflow and efficient project management. In addition, thoroughly testing
methods on pilot or limited-scope projects before full implementation is important. Conducting extensive map checks and QC procedures ensures data accuracy and meets the expectations of engineers and designers. Moreover, communicating the capabilities and limitations of different Lidar collection methods to engineers and designers is important. This helps in making informed decisions about the best method for specific projects and managing expectations.
For training and professional development, WisDOT relies heavily on webinars and other online resources. While dedicated Lidar-specific training is limited to the team lead, there is an emphasis on internal knowledge sharing. The organization maintains a GIS database of collected projects, which helps engineers and designers access historical data and determine if updates are necessary. WisDOT is also exploring new static Lidar instruments to keep their technology up-to-date and maintain a high level of proficiency within the team.
Future Plans and Initiatives
WisDOT has engaged in notable collaborations with academic institutions to enhance their Lidar technology applications. In 2013, WisDOT collaborated with the University of Wisconsin’s Construction and Materials Support Center on a 3D technologies implementation plan, which significantly influenced their current use of Lidar. In 2016–2017, they partnered with the University of Wisconsin Traffic Operations and Safety (TOPS) Lab to explore the feasibility of statewide mobile Lidar collection for vertical bridge clearances and asset management. Although the project did not proceed statewide, valuable insights were gained.
To address scalability as data volume grows, WisDOT is transitioning from internal servers to cloud-based storage solutions. This move aims to manage their expanding data efficiently, which currently stands at around 95 terabytes and continues to grow.
Future research beneficial to WisDOT includes further exploration of UAS Lidar applications. They are particularly interested in learning how other state DOTs utilize Lidar data, potentially adopting successful strategies to enhance their practices.
WisDOT emphasizes the importance of sharing knowledge and practices among state DOTs to avoid redundancy and improve efficiency. They anticipate that the synthesis project will provide valuable insights into the innovative uses of Lidar by other DOTs, fostering a collaborative approach to technological advancements. Overall, WisDOT remains committed to leveraging Lidar technology effectively, focusing on continuous improvement and adaptation to meet future challenges and opportunities.
Highlights
WisDOT has utilized Lidar technology extensively, starting with static Lidar projects and expanding to aerial Lidar and imagery since 2016. They collect data for 20–50 projects annually and use static Lidar for specific needs such as emergency response and wall monitoring. WisDOT has recently used mobile Lidar for pavement crack analysis projects. Aerial Lidar provides substantial benefits, allowing coverage of large areas with high-density data. The integration of aerial imagery and Lidar has reduced costs and improved accuracy, especially for hard surfaces. Major challenges include communicating Lidar capabilities across DOT units and ensuring staff are trained to use the data. Managing and storing a vast amount of data is also a noteworthy issue.
In WisDOT, Lidar data is stored on servers and is transitioning to commercial cloud-based services. Data is reduced to final surfaces before sharing with project teams. Automation software tools have improved processing efficiency. WisDOT bundles aerial imagery and Lidar collection to save costs. They have robust QA/QC processes and emphasize thorough testing and clear communication of data accuracy and collection methods.
Collaborations with academic institutions have enhanced WisDOT’s Lidar capabilities. WisDOT is also interested in exploring UAS Lidar applications. Knowledge sharing with other state DOTs is valued to identify effective procedures. Overall, WisDOT is focused on improving data management, staff training, and exploring innovative Lidar applications to enhance transportation projects.
Colorado DOT
Background
The Colorado Department of Transportation (CDOT) has been integrating Lidar technology into their operations primarily through regional contracting. The Information Management Branch, specifically the GIS section, manages remote sensing data collection and integration. While the Lidar work is generally contracted out, there has not been a consistent follow-up on the technology post-delivery. The regions receive comprehensive data including raw data, GIS, surface models, and other deliverables. CDOT’s Lidar applications have been mainly terrestrial, mounted on trucks. This technology has been used for various purposes such as vertical clearance data collection and surveying small highway stretches.
Benefits and Challenges
CDOT acknowledges the potential benefits of Lidar technology, particularly for managing Interstate and local state highways. Although they have not yet experienced these benefits on a large scale, CDOT recognizes the value in applications such as asset extraction. A pilot study conducted in 2017 on a 25-mile stretch of highway successfully extracted 21 asset types, demonstrating Lidar’s utility in detailed asset management.
CDOT faces several challenges in leveraging Lidar technology, including cost, technical knowledge, and leadership. Initial attempts to implement a statewide Lidar project highlighted these issues, particularly the financial burden and the need for specialized expertise. To address these challenges, CDOT conducted a pilot study, which provided valuable insights and experience. Despite the positive outcomes of the pilot, broader implementation has stalled. However, there has been a renewed interest and push toward operationalizing Lidar technology within CDOT, signaling a potential shift toward overcoming these challenges.
Data Management
Currently, CDOT does not manage large statewide Lidar databases but anticipates this need in the future. They are preparing for extensive data management by meeting with various asset groups to understand and plan for these requirements. Also, at this stage, CDOT does not have specific practices for archiving Lidar data, significant IT investments for Lidar data management, or a defined process for data mining and automation tools. The organization also does not have established procedures for ensuring the interoperability of Lidar data with other geospatial datasets and systems. However, these considerations are part of their forward-looking strategy as they aim to integrate Lidar technology more fully into their operations.
Experience Gained
Because of the early stages of Lidar implementation at the CDOT, specific practices, QA measures, lessons learned, and professional development strategies related to Lidar technology are not yet well-defined. CDOT has not yet established specific practices to make the business case for Lidar data use or seen substantial examples of high ROI because of the limited deployment
of Lidar technology. Likewise, CDOT does not have specific QA practices in place for Lidar data. While CDOT is in the initial phase of adopting Lidar technology, they have recognized the importance of pilot projects in understanding the scope and potential benefits before full-scale implementation. CDOT has not yet developed formal training or professional development programs for staff working with Lidar technology.
Future Plans and Initiatives
CDOT has proposed collaborations with academic institutions and research bodies to develop a remote sensing and GIS center. This initiative aims to create a standardized approach to Lidar and other remote sensing technologies across various asset groups within CDOT. Currently, different groups within CDOT conduct independent activities without a unified strategy. The proposed center would centralize data hosting, provide modeling infrastructure, and offer training and assistance, thereby promoting consistency and efficiency.
CDOT recognizes the need for scalability as the volume of Lidar data grows. They plan to transition from internal servers to cloud storage solutions to manage the increasing data volume. Additionally, CDOT aims to integrate drones with Lidar payloads to complement terrestrial data collection, allowing for more comprehensive and scalable data acquisition strategies.
Future research beneficial to CDOT includes advancements in UAS Lidar technology and automated feature extraction processes. CDOT is interested in understanding how other state DOTs use Lidar data to identify potential improvements and new applications for their Lidar initiatives.
Highlights
CDOT is integrating Lidar technology through regional contracts managed by the GIS section within the Information Management Branch. Most Lidar applications have been terrestrial, mounted on trucks, or used for vertical clearance data collection and small highway surveys. CDOT faces challenges such as cost, technical expertise, and leadership support, which have hindered broader implementation. Currently, CDOT lacks comprehensive data management practices for Lidar but is preparing for future needs by consulting with various asset groups. There are no specific practices for archiving Lidar data or ensuring interoperability with other geospatial datasets. Protocols, QA measures, and formal training programs for Lidar technology are not yet established at CDOT. Future plans include collaborating with academic institutions to develop a remote sensing and GIS center, transitioning to cloud storage solutions, and integrating drones with Lidar payloads.
Texas DOT
Background
The TxDOT has been progressively integrating Lidar technology into its operations, marking a significant shift from its traditional reliance on photogrammetry. Historically, TxDOT utilized photogrammetry for most of its mapping and surveying needs, but recent years have seen a concerted effort to adopt more advanced remote sensing technologies. This transition has been driven by the need for higher spatial density in survey data to support more precise engineering designs. Consequently, TxDOT has mandated the use of Lidar on all new projects to meet these enhanced requirements. This initiative has included comprehensive training programs to equip district personnel with the necessary skills to operate static Lidar systems, which serve as the foundational technology for more advanced mobile Lidar applications.
In parallel with these internal advancements, TxDOT has worked closely with consultants who have long utilized Lidar technology, leveraging their expertise to accelerate its adoption. TxDOT’s photogrammetry section, now rebranded as the Remote Sensing Services Section, has expanded its remit to include Lidar scanning, reflecting the growing importance of this technology. Furthermore, TxDOT is in the process of acquiring several mobile Lidar systems and UAS Lidar platforms, aiming to enhance its data collection efficiency and accuracy. These efforts are part of a broader statewide initiative to conduct comprehensive asset scanning and implement automated image recognition for various infrastructure elements, thereby improving compliance with federal requirements and streamlining maintenance processes.
TxDOT’s innovative approach includes exploring the integration of Lidar control points directly into roadway structures. This forward-thinking strategy aims to enhance the accuracy and efficiency of Lidar scans while minimizing the need for survey crews to work in dangerous roadside environments. By embedding permanent control points into bridges and other structures during construction, TxDOT hopes to facilitate real-time mapping capabilities, significantly reducing the time and effort required for subsequent surveys. This initiative is still in its early stages, with ongoing efforts to address the associated challenges and refine the implementation process. Despite being relatively new to Lidar, TxDOT’s proactive and systematic approach underscores its commitment to leveraging advanced technologies to improve transportation infrastructure management and safety.
Benefits and Challenges
The integration of Lidar technology within TxDOT has brought several substantial benefits, particularly in terms of safety, data density, and efficiency. The use of terrestrial laser scanning for topographical surveys has significantly reduced the need for surveyors to be physically present on busy roadways, thereby enhancing safety and minimizing the risk of accidents. This remote sensing technology enables the collection of high-density data, allowing TxDOT’s digital delivery team to design projects with a level of detail that would be impossible with traditional survey methods. The ability to design every inch of a project using high-density Lidar data is a significant advantage, as it ensures comprehensive coverage and precision. Additionally, Lidar technology has led to considerable efficiency gains, both in terms of data collection and potential future processing improvements through the application of AI and ML techniques. The ability to automate the extraction and processing of assets from Lidar and imagery data promises further enhancements in productivity, although human oversight in QC remains essential.
Another benefit of Lidar technology is its versatility and the range of applications it supports. For instance, TxDOT has seen substantial benefits from using Lidar for creating as-built models of infrastructure, which support maintenance activities and planning efforts for future projects. Lidar data’s ability to provide accurate, high-resolution models allows for better decision-making and more efficient project execution. The technology is also being utilized in various innovative ways, such as incorporating permanent reference markers into roadway structures. These markers can be used by mobile mapping systems and intelligent software to identify control points, thus facilitating faster, safer, and more accurate scanning operations. This initiative reflects TxDOT’s commitment to leveraging Lidar technology for long-term, sustainable improvements in infrastructure management.
TxDOT faces several challenges in the widespread adoption and effective use of Lidar technology. One significant issue is the proper use and understanding of the technology. Misuse or improper application of Lidar by inexperienced personnel or contractors can lead to inaccuracies and undermine trust in the technology. For instance, discrepancies between Lidar data and GPS data because of inadequate metadata can cause skepticism among district engineers. Moreover, the perceived complexity and high initial costs of Lidar technology can be deterrents to its adoption. TxDOT has encountered resistance from some engineers who are accustomed to traditional
methods and are hesitant to embrace new technologies without a clear understanding of their benefits and proper training.
Bureaucratic hurdles also pose a challenge. Lengthy procurement processes for contractor services can delay data collection and response times. This delay can be particularly problematic when quick data collection is needed for urgent projects or when verifying the quality of ongoing construction work. To address these issues, TxDOT is developing internal capabilities to complement external contractor services, enabling quicker responses and reducing reliance on external entities for data collection tasks. This approach ensures that TxDOT can promptly address data collection needs without being hampered by external dependencies.
Data management is another significant challenge. The vast amounts of data generated by Lidar surveys require robust storage solutions and efficient data processing capabilities. Ensuring that data storage and processing solutions are both cost-effective and capable of handling large volumes of data is a complex but necessary task. TxDOT is actively working on developing enterprise storage solutions that allow seamless extraction and use of data by both GIS professionals and engineers. However, current software solutions and vendor approaches do not always align with TxDOT’s requirements, necessitating ongoing collaboration and negotiation to develop effective cloud computing strategies. Furthermore, the need for efficient data management extends to ensuring that data remains accessible and usable for various applications, from project planning and design to maintenance and emergency response.
Education and training are critical components to overcome these challenges. TxDOT emphasizes the need for thorough training and education on Lidar technology, ensuring that staff can ask the right questions and specify their data needs accurately. This training is not limited to technical aspects but also includes understanding the strategic benefits and applications of Lidar technology. By fostering a culture of continuous learning and adaptation, TxDOT aims to build a knowledgeable workforce capable of fully leveraging the potential of Lidar technology. Moreover, by working closely with software vendors and other technology providers, TxDOT seeks to influence the development of solutions that meet its specific needs and operational contexts.
Data Management
The management of Lidar data within TxDOT is a complex and evolving process, characterized by diverse protocols and varying levels of sophistication across different districts. Historically, Lidar data management at TxDOT has faced significant challenges. Some districts have relied on local storage solutions such as USB drives and hard drives, which are prone to failure over time. This decentralized approach has led to inconsistencies and inefficiencies in data accessibility and longevity. Despite attempts to establish a statewide data management solution, finding a cloud-based system that meets TxDOT’s specific needs has been elusive. TxDOT has experimented with several solutions, including a pilot solution in 2015, which was ultimately deemed unsuccessful because of cumbersome data transfer processes and user interface issues.
Currently, TxDOT employs a mix of solutions for Lidar data management. The primary system has been limited because of template and upload process limitations. Efforts are ongoing to work with vendors to improve the system’s functionality. Additionally, TxDOT is evaluating new platform solutions, which have shown promise in handling geocoding, geo-tagging, and inherent metadata configurations. TxDOT continues to rely on dedicated servers for the photogrammetry division, and even internal platforms for UAS program data management. These disparate systems highlight the ongoing struggle to find a cohesive and efficient data management strategy.
Data sharing within TxDOT is primarily isolated, with Lidar data mostly confined to the survey and GIS departments. This limited sharing is because of the technical complexity of Lidar data, which many user groups within the organization are not equipped to handle. Efforts are being
made to train construction inspectors and designers to utilize Lidar data, which could significantly enhance construction inspection efficiency and project design accuracy. For instance, tools are used for automated feature extraction, such as curb detection, although the effectiveness of these tools depends heavily on the technical skills of district surveyors.
Automation and data interoperability remain areas of active exploration for TxDOT. The department uses image analysis software and has pilot programs integrating ML with Lidar and photogrammetry data for tasks such as sign and culvert recognition. Standards for data interoperability are important, and TxDOT’s contracts with professional engineering providers stipulate specific deliverables to ensure data formats are consistent and usable across various platforms. Internal initiatives, such as the Journey to Enterprise Data Integration program, aim to develop schemas and governance structures to ensure interoperability across TxDOT’s systems.
Experience Gained
In the realm of Lidar technology adoption, TxDOT has navigated various challenges and accumulated valuable insights. One key lesson learned is the importance of securing strong support from senior management. TxDOT’s leadership recognizes the potential efficiencies and innovations offered by Lidar, which has fostered a supportive environment for obtaining the necessary funding for hardware, software, and training. However, the state procurement process remains a significant obstacle, often taking up to 2 years to acquire new equipment. This process is further complicated by stringent IT security requirements, particularly concerning hardware interactions within TxDOT’s ecosystem.
QA and the management of metadata are other areas where TxDOT has focused its efforts. TxDOT has developed checklists and standards to ensure the quality of data generated from Lidar. This includes not only verifying the point clouds but also ensuring that the metadata associated with GPS and control checks are accurate. The goal is to develop standardized reports and automated scripts to facilitate QC, given the high volume of data and the limited pool of skilled personnel.
The interviewees highlighted several benefits they have observed associated with the use of Lidar technology. Safety is a paramount concern, with Lidar significantly enhancing the safety of field personnel by reducing the need for them to work on roadways. This not only protects TxDOT workers but also minimizes disruptions to the traveling public. Another major benefit is the reduction of change orders, as Lidar data allows for quick and accurate comparisons between design plans and actual conditions. This capability is particularly valuable in identifying discrepancies early, thus preventing costly adjustments during construction. Additionally, the ability to perform clash detection ensures that potential conflicts in design are identified and resolved before they result in delays or additional expenses.
Training and proper use of technology are necessary for successful Lidar implementation. Ensuring that staff are well-trained in both the operation and application of Lidar technology helps maximize its benefits. TxDOT has recognized the need for continuous training programs that evolve with technological advancements. This includes incorporating practical applications, data governance, and safety protocols into the training curriculum. Moreover, leveraging vendors and manufacturers for basic training, while maintaining in-house expertise on TxDOT-specific procedures, ensures that the TxDOT’s personnel are well-prepared to handle the technology effectively.
The top experiences gained by TxDOT emphasize the importance of using appropriate control and check points, providing comprehensive training, and ensuring that consultants involved in Lidar projects have the necessary expertise. TxDOT has observed that issues often arise from improper use of control points, highlighting the need for skilled personnel who can accurately
manage these aspects. Furthermore, integrating 3D modelers and geodetic surveyors into project teams enhances the ability to address modeling and control challenges effectively.
Future Plans and Initiatives
TxDOT has laid out a comprehensive set of future plans and initiatives aimed at leveraging advanced Lidar and UAS technologies to enhance their operations and infrastructure management. Collaboration with academic institutions forms a significant part of these plans. Currently, TxDOT is engaged in three notable studies. One study conducted by Texas A&M University-Corpus Christi explores the potential of UAS technology to improve data collection methods.
Another study at Texas A&M University-College Station’s Texas A&M Transportation Institute (TTI) involves investigating photogrammetry and scanning specifically for utility mapping. This study aims to improve the accuracy and efficiency of mapping utilities before they are covered up. Additionally, TTI is conducting a cost-benefit analysis of survey tolerances, assessing whether traditional survey standards or newer technologies provide better value. This analysis is particularly relevant for evaluating the financial implications of change orders versus the costs of more precise surveying methods.
In terms of future research topics, TxDOT is interested in several innovative areas. Automated image recognition for road features is high on the list, with the goal of achieving near real-time mapping. Another area of interest is embedding survey control directly into roadway structures, which could streamline the process of setting up control points and enhance the accuracy and efficiency of Lidar surveys. Additionally, TxDOT is exploring the development of a GIS-based indexing system to manage and access Lidar data more effectively. This system would allow users to navigate to specific areas of interest without loading extensive datasets, thereby improving data accessibility and usability.
Moreover, TxDOT envisions long-term advancements such as automated mapping systems. These systems could potentially use UAS to conduct mapping missions autonomously, significantly reducing the need for personnel to be physically present in the field. This vision includes the possibility of deploying UAS from area offices, performing surveys, and uploading data directly to workstations, all without human intervention on-site.
TxDOT is also focusing on the development of synthetic datasets for AI and ML applications. This initiative aims to create large computational models to process bulk Lidar data for extracting features and assets relevant to flood resiliency and other infrastructure needs. These efforts highlight TxDOT’s commitment to integrating advanced technologies and innovative research into their operational framework, ensuring that they remain at the forefront of transportation infrastructure management.
Highlights
TxDOT has been progressively integrating Lidar technology into their operations, with substantial advancements and notable benefits. TxDOT has employed Lidar across various applications, emphasizing safety and data density, which are important for their digital delivery initiatives. Lidar technology has significantly reduced the need for personnel to be in potentially unsafe environments, thereby enhancing safety. Furthermore, Lidar’s high spatial density supports more precise project designs, contributing to efficiency gains and better resource management.
However, challenges persist, particularly with the misuse of technology and the lack of standardized training. TxDOT has encountered issues with inconsistent data accuracy because of improper usage and varying levels of expertise among consultants and district engineers.
To address these challenges, TxDOT is developing comprehensive training programs and establishing clear QA protocols. Additionally, TxDOT is exploring innovative data management solutions, such as cloud-based systems and automated data processing tools, to enhance data sharing and interoperability across departments. Collaborations with academic institutions, including Texas A&M University and TTI, are pivotal in advancing these initiatives, focusing on areas like UAS data collection, photogrammetry, and automated image recognition.
Summary
The integration of Lidar technology across state DOTs has brought significant advancements and challenges. This chapter highlighted the experiences of Oregon, Tennessee, Wisconsin, Colorado, and Texas DOTs providing insights into their unique approaches and common challenges in utilizing Lidar technology. These highlights are summarized in Table 12.
Differences and Time Gaps
The implementation timelines for Lidar technology vary significantly among the DOTs. Oregon has been a pioneer, utilizing Lidar since 2002, while Tennessee started in 2009, Wisconsin began integrating aerial Lidar in 2016, Texas has recently started incorporating Lidar with a comprehensive training and deployment strategy, and Colorado is still in the early stages of adoption. These differences highlight the varying levels of maturity and experience with Lidar technology across state DOTs as well as across the different divisions within a DOT. Each state has adopted different strategies based on their situation resulting in unique needs and challenges. Oregon and Wisconsin have focused on comprehensive data management and robust QA/QC practices. In contrast, Colorado is currently developing a centralized approach through a proposed remote sensing and GIS center, aiming for standardized and consistent Lidar data management. Texas is focusing on embedding Lidar control points into roadway structures and enhancing real-time mapping capabilities.
Collaboration with academic institutions and research entities has been a common theme. For instance, Oregon has worked with OSU and Oregon Institute of Technology, while Wisconsin has collaborated with the University of Wisconsin TOPS Lab. Texas has partnered with Texas A&M University and its TTI for studies on UAS data collection and photogrammetry. These partnerships have been instrumental in advancing Lidar technology and developing innovative solutions.
Common Benefits
One of the most significant benefits of Lidar technology is the enhanced safety for personnel, as it allows data collection without needing to be physically present in challenging areas. For example, Tennessee has used Lidar to survey landslides and rockfalls, significantly improving safety and efficiency. In addition, Oregon’s use of mobile and UAS Lidar exemplifies the precision and reliability that Lidar brings to transportation projects. State DOTs have found innovative uses for Lidar data beyond traditional applications. For example, Tennessee used Lidar data to determine billboard locations, and Wisconsin employed Lidar for concrete pavement crack analysis. Texas has utilized Lidar for real-time mapping and the incorporation of permanent control points in roadway structures to improve safety and efficiency in construction inspections. These innovative applications demonstrate the versatility and potential of Lidar technology.
Common Challenges
All state DOTs highlighted the significant financial investment required for Lidar technology. Initial costs for equipment, software, and data storage infrastructure can be substantial, as seen
Table 12. Summary of the case examples.
| Background | Benefit | Challenge | Data Management | Experience Gained | Future Plan and Initiative | |
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| Oregon DOT |
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| Tennessee DOT |
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| Wisconsin DOT |
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| Colorado DOT |
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| Texas DOT |
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in Oregon and Tennessee. Finding sustainable funding sources remains a challenge, as evidenced by Colorado’s stalled statewide Lidar project because of financial constraints. A recurring theme is the need for specialized technical knowledge to process and utilize Lidar data effectively. DOTs such as Colorado and Tennessee emphasized the necessity of continuous professional development and training programs to keep staff updated on the latest Lidar technologies and methodologies. Texas has faced issues with fragmented data management and is currently evaluating comprehensive cloud-based solutions to improve data accessibility and integration. Colorado, in particular, has not yet developed formal training programs but recognizes their importance.
Managing the vast amounts of data generated by Lidar is a significant challenge. Oregon and Wisconsin highlighted their strategies for data storage, including the use of cloud solutions to handle the growing data volumes. Colorado is preparing for extensive data management needs and plans to transition to cloud storage solutions.
Ensuring that Lidar data integrates seamlessly with other geospatial datasets and systems can help yield more value. This challenge includes maintaining metadata integrity and ensuring compatibility with state and federal reporting requirements. States are exploring various solutions, including custom scripts and metadata enhancement techniques, although Colorado has not yet established procedures for this.
Automating the extraction of features from Lidar point clouds remains a significant hurdle. While state DOTs such as Oregon and Tennessee have invested in semi-automated software, fully automated solutions are still a work in progress. Texas is particularly interested in advancing automated image recognition for road features and integrating survey control directly into structures. Research and development in ML and AI are areas of ongoing interest.
Looking Forward
Future research priorities include developing more efficient asset extraction techniques and advancing automation through ML and AI. For example, Oregon is exploring blue-green Lidar for hydraulic engineering, while Tennessee is investigating using pocket Lidar for construction inspections. Texas is focusing on integrating automated image recognition for road features and embedding survey control directly into structures. Colorado is interested in advancements in UAS Lidar technology and automated feature extraction processes.
As Lidar data volumes grow, DOTs are exploring scalable solutions for data storage and management. Cloud-based storage solutions are becoming more prevalent, as seen with Wisconsin and Colorado, to handle the increasing data load efficiently. Texas is evaluating comprehensive cloud-based solutions to improve data accessibility and integration across various departments.
Developing standardized procedures for Lidar data collection, processing, and management is essential. Colorado’s proposed remote sensing and GIS center aims to create a unified strategy for Lidar and other remote sensing technologies across various asset groups.
DOTs continuously find new and innovative uses for Lidar technology. For instance, Oregon utilized Lidar for wildfire recovery efforts, and Tennessee is exploring its use for detecting pavement conditions and potholes. Texas is also investigating its use for flood resiliency and real-time mapping initiatives. This adaptability underscores the vast potential of Lidar technology for diverse applications.
