Practices for Collecting, Managing, and Using Light Detection and Ranging Data (2025)
Chapter: Summary
SUMMARY
Practices for Collecting, Managing, and Using Light Detection and Ranging Data
The emergence of light detection and ranging (Lidar) technology marks a transformative era in transportation infrastructure management by providing high-resolution, accurate, three-dimensional (3D) data to support diverse applications such as topographical surveys, asset management, and traffic safety. The detailed information provided by Lidar enhances precision, efficiency, and safety compared with many other data collection techniques. By regularly capturing Lidar scans of transportation infrastructure, such as bridges and roadways, departments of transportation (DOTs) can proactively monitor degradation, prioritize maintenance efforts, and make informed decisions to extend the lifespan of their assets.
This synthesis comprehensively reviews and documents the current practices of state DOTs concerning the technical, administrative, policy, and operational aspects of collecting, managing, and utilizing Lidar data. The synthesis explores the types of projects and business uses of Lidar, including: roadway projects, operation, maintenance, safety, research, mapping, asset management, and more; data sources, data collection, processing, extraction, storage, and maintenance, and the identification of expected potential uses of data; data mining for various uses, including addressing challenges such as expected accuracy levels based on purposes, attachment and permanence of accuracy level information to the data, integration with software tools, and others; data management and governance practices; quality assurance and quality control (QA/QC) practices; and DOT policies and standards related to Lidar data collection/maintenance.
Information was gathered through a literature review, a survey of state DOTs, and five case example interviews. The literature review highlights the evolution of Lidar technology from its inception to its current state-of-the-art applications in transportation. It explores different types of Lidar systems—ranging from airborne systems to map large areas to pocket Lidar technology to map small objects—and their respective strengths and limitations in diverse use cases. The review also examines data sources and methodologies for integrating and processing Lidar data within existing geographic information system (GIS) frameworks. The survey was distributed to 50 state DOTs and the DOT of the District of Columbia and received a 100% response rate. The case examples summarize information gleaned from interviews with five state DOTs, from Oregon, Tennessee, Wisconsin, Colorado, and Texas.
The synthesis found that all DOTs have utilized Lidar, although this ranges from limited to prolific usage throughout the organization. Nearly half of the DOTs (49.0%) have used Lidar technology for over a decade, with applications ranging from basic topographic surveys to complex 3D modeling for urban planning. In contrast, four DOTs responded that they are not currently using the technology; however, examples of some Lidar usage and/or plans to utilize Lidar data were discovered for all four. Survey responses also indicate that many
DOTs prioritize robust QA/QC processes, effective data management, and secure storage systems. Common challenges include the high initial costs of Lidar technology, the need for specialized training, and difficulties in integrating large volumes of Lidar data with existing systems. Nevertheless, many DOTs indicated that they achieved significant return on investment (ROI) with Lidar technology through advantages such as safety improvements (70.6%) and time efficiency (68.8%). Several DOTs are exploring automation and machine learning to improve data processing workflows.
The report also provides findings from detailed case examples from the DOTs from Oregon, Tennessee, Wisconsin, Colorado, and Texas. These case examples provide real-world insights into the operational, technical, and administrative challenges encountered by the state DOTs, as well as the solutions and benefits realized by using Lidar. According to the case examples, Oregon DOT has been a pioneer in the use of Lidar since 2002, integrating it into nearly all aspects of its operations, from design and construction quality control to slope stability and road safety. Tennessee DOT highlighted Lidar’s safety advantages, particularly in applications such as landslide and rockfall assessments. However, both Oregon and Tennessee face challenges related to extensive data processing and improving data sharing. Wisconsin DOT indicated that binding data collection efforts by using Lidar reduces costs. Colorado DOT, still in the early stages of Lidar adoption, is preparing for broader implementation, including the use of uncrewed aerial systems (UAS) technology. Texas DOT, meanwhile, has made significant advancements in the use of mobile and UAS Lidar, emphasizing real-time mapping and safety improvements. They are also tackling data management challenges through cloud-based solutions.
In conclusion, the literature review provides numerous examples of the benefits of using Lidar to support diverse applications in design, construction, asset management, maintenance, operations, and safety. The survey responses show that although Lidar usage is widespread across the DOTs, the level of implementation varies. Some states have developed robust standards while others are exploring the expanded use of Lidar. The case examples demonstrate the importance of strategic investment and careful integration of Lidar into transportation workflows to maximize its utility and overcome challenges such as data management and processing.
