Practices for Collecting, Managing, and Using Light Detection and Ranging Data (2025)
Chapter: 1 Introduction
CHAPTER 1
Introduction
The integration of light detection and ranging (Lidar) technology into transportation systems represents a pivotal evolution in the management of infrastructure. By offering high-resolution, three-dimensional (3D) data, Lidar technology enhances the precision, completeness, and efficiency of data to support numerous transportation processes, from highway design to traffic safety management. A key benefit that has led to high returns on investment (ROI) is the versatility of Lidar datasets to support a wide range of applications. As state DOTs increasingly adopt this technology, a comprehensive synthesis is essential to understand its diverse applications and associated implications fully. This report addresses this need by documenting how DOTs can effectively leverage Lidar technology to transform their operations and infrastructure management practices.
The chapter details the specific objectives of the synthesis, which aim to provide documentation of current practices around the collection, management, and utilization of Lidar data within DOTs. The methodology employed to gather data for this synthesis is also outlined. A glossary of key terms clarifies the terminology used throughout the report. The chapter concludes with an overview of the report’s organization, which serves as a roadmap to guide the reader through the subsequent chapters.
Background
Despite its extensive benefits and opportunities, Lidar technology presents several challenges that state DOTs must navigate. One of the primary hurdles is the technology’s integration into existing geographic information system (GIS) frameworks or building information models (BIMs). Effective integration requires significant initial investment, both in terms of finance and time, as well as demands on ongoing data governance investments to maximize ROI. Additionally, the sheer volume of data produced by Lidar systems necessitates modifications to DOT practices, including the development of sophisticated methods for data storage, processing, and utilization. These challenges are compounded by the need for specialized training for personnel, ensuring that the workforce can effectively manage, access, interrogate, and interpret Lidar data.
Further complicating the use of Lidar are the operational challenges faced by DOTs, which vary widely across different regions and projects. These challenges can include logistical issues such as the deployment of Lidar equipment in diverse terrain and environments, the calibration of systems to ensure data accuracy, extraction of metrics, and the integration of Lidar data with other traffic and environmental data streams. The variability in Lidar applications also leads to disparities in data management practices, which can delay the seamless exchange of information and experience gained both within a DOT and between different DOTs.
This synthesis documents the diverse experiences of DOTs, identifying common challenges and exemplary practices that can guide future implementations of Lidar technology. By documenting these experiences, the synthesis prevents the unnecessary repetition of efforts and the “reinvention
of the wheel,” facilitating a more streamlined and effective adoption of Lidar across the transportation sector.
Synthesis Scope and Objectives
The objective of this synthesis is to comprehensively review and document the current practices of state DOTs concerning the technical, administrative, policy, and operational aspects of collecting, managing, and utilizing Lidar data. This report comprehensively addresses several aspects of Lidar data collection, management, and usage by state Departments of Transportation (DOTs). The key topics covered in this report include the following:
- Types of projects and business uses: The report explores the application of Lidar data in a variety of projects and business uses, including roadway projects, operation, maintenance, safety, research, mapping, asset management, and other uses.
- Data sources, collection, uses, and maintenance: The report provides an in-depth examination of the different sources of Lidar data, detailing the processes involved in data collection, processing, extraction, storage, and maintenance. The identification of expected potential uses of the data is also included.
- Data mining for various uses: The report addresses the challenges and methodologies associated with data mining for various applications, such as expected accuracy levels based on specific purposes, attachment and permanence of accuracy level information to the data, integration with software tools, and other challenges.
- Data management and governance practices: The report provides a detailed look into the practices surrounding the management and governance of Lidar data, ensuring its integrity and usability across different applications.
- Quality assurance and quality control practices: The report outlines the quality assurance and control measures necessary to maintain the accuracy and reliability of Lidar data to promote trust in its reuse for maximum ROI.
- DOT policies and standards: The report provides an overview of the DOT policies and standards related to the collection and maintenance of Lidar data, highlighting the regulatory framework guiding these practices.
- Repository of case examples: Real-world examples of effective Lidar implementations are compiled to provide valuable insights for state DOTs.
Note that related technologies, such as structure from motion (SfM) multi-view stereopsis (MVS) photogrammetry, are not within the scope of the synthesis. While SfM/MVS photogrammetry is complementary to Lidar in many cases, Lidar technology prevails in terms of its higher accuracy, ability to penetrate openings in vegetation to provide bare earth topographic information, and improved acquisition in challenging lighting conditions, given that Lidar is an active sensor. Nevertheless, the integration of SfM/MVS photogrammetry and other remote sensing technologies with Lidar can enhance the overall quality and comprehensiveness of geospatial data. This complementary approach leverages the strengths of each technology, resulting in more robust and multifaceted datasets that can address a wider array of needs and applications. Notably, much of the information in this report is applicable to SfM/MVS photogrammetric technologies, which can provide similar data products and support similar applications. SfM/MVS photogrammetry technology also faces similar challenges in data mining and management.
Study Approach
This comprehensive approach encompasses a literature review to document the state of the art, detailed online questionnaires to state DOTs to document the state of practice, and targeted follow-up interviews to document current practices and experience gained.
Literature Review: The synthesis commenced with an extensive review of existing literature related to the collection, management, and application of Lidar data within transportation projects. This review spanned a wide range of sources, including peer-reviewed journals, conference proceedings, DOT reports, and other relevant publications. The literature review aimed to establish a baseline understanding of the current state of Lidar technology, documenting innovations and ongoing challenges, as well as documenting the evolution of practices over time. This foundation enables an assessment of how Lidar data integrates into broader GIS and supports transportation infrastructure management.
Questionnaires Distributed to State DOTs: To directly measure the current practices and experiences of DOTs with Lidar, a detailed online questionnaire was distributed to DOTs. The questionnaire sought to capture diverse perspectives on Lidar data usage, focusing on aspects such as data collection techniques, processing tools, integration challenges, and the overall impact of Lidar on operational efficiency. The high response rate (100%) provided significant insights, highlighting varying levels of adoption as well as practical implications of Lidar technology in day-to-day and strategic contexts.
Follow-up Interviews with Selected State DOTs: Building on the questionnaire results, followup interviews via virtual meetings were conducted with selected DOTs identified as either leaders in Lidar technology application or those facing unique challenges. These interviews provide deeper insights into specific practices implemented by DOTs.
Together, these steps not only furnished comprehensive data on current Lidar applications but also illuminated the paths through which DOTs navigate technological advancements and integration challenges. The findings from these diverse sources are synthesized to provide an understanding of Lidar’s role in enhancing transportation infrastructure management across the United States.
Terminology
This section presents a curated list of common terms and definitions (Table 1) used throughout this synthesis to ensure clarity and consistency in understanding the technical, administrative, and policy-related discussions related to Lidar technology and its applications. These definitions facilitate the improved understanding of the complex technologies and methodologies discussed as well as aid in the effective communication of key concepts among professionals from diverse backgrounds.
Table 1. List of terms and definitions used throughout this synthesis report.
| Term | Definition |
|---|---|
| Airborne Lidar | Lidar systems that operate from the air, typically mounted on aircraft. |
| Asset Management | Systematic process of developing, operating, maintaining, upgrading, and disposing of assets cost-effectively. |
| Calibration | The process of configuring an instrument to reduce systematic error and provide measurements within an acceptable range. |
| Cloud Computing | Data are stored and managed on remote computers “in the cloud.” These computers are owned and operated by others and connect to users’ computers by means of the Internet. |
| Data Analysis | The process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information. |
| Data Extraction | The process of retrieving specific data from structured or unstructured sources. |
| Data Governance | The overall management of the availability, usability, integrity, and security of the data employed in an organization or enterprise. |
| Data Integration | Combining data from different sources to provide a unified view or analysis. |
| Term | Definition |
|---|---|
| Data Life Cycle | The sequence of stages that a particular unit of data goes through from its initial generation or capture to its eventual archival or deletion at the end of its useful life. |
| Data Maintenance | The processes involved in keeping data up-to-date and organized properly. |
| Data Management | The process by which data are acquired, validated, stored, protected, and processed. |
| Data Mining | The process of discovering patterns and knowledge from large amounts of data. |
| Data Processing | Techniques and methods of transforming raw data into meaningful information through a process. |
| Data Scalability | The capability of a system to handle a growing amount of data or its potential to be enlarged to accommodate that growth. |
| Data Security | Protective measures applied to prevent unauthorized access to computers, databases, and websites. |
| Dataset | A collection of data, usually presented in tabular form. |
| Data Storage | The recording (storing) of information (data) in a storage medium. |
| DOT Policies, Guidelines and Standards | Regulations and guidelines established by DOT that govern Lidar data collection and maintenance. |
| Georeferencing | The process of relating internal data spatially into a known geographical coordinate system. |
| Geospatial Information | Information that identifies the geographic location and characteristics of natural or constructed features and boundaries on the earth. |
| GIS | Systems designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data. |
| High Definition (HD) Maps | Detailed maps of features and geometry in the streetscape to support autonomous vehicle navigation. |
| Infrastructure | The fundamental facilities and systems serving a country, city, or area. |
| Lidar (Light Detection and Ranging) | A remote sensing method that uses light in the form of a pulsed laser to measure distances to objects at high rates to generate 3D geometric information. |
| Machine Learning (ML) Algorithms | Algorithms that enable software to make predictions or perform analyses by learning from patterns in data. |
| Mobile Lidar | Lidar systems that are mobile, usually mounted on vehicles, allowing efficient data collection from varying locations (Olsen 2013). |
| Policy Standards | Official guidelines that dictate how certain operations should be performed. |
| Quality Assurance (QA) | Process to ensure data or products meet specified standards through systematic activities (TRB 2018). |
| Quality Control (QC) | Process to monitor, assess, or adjust production to verify the quality of data products meets requirements by measuring compliance relative to standards. Techniques to verify product quality by identifying defects and implementing corrections (TRB 2018). |
| Remote Sensing | The science of obtaining information about objects or areas from a distance, typically from aircraft or satellites. |
| ROI | A financial metric evaluating the profitability of an investment, measuring the return relative to its cost. |
| Roadway Projects | Projects that involve the construction, maintenance, and operation of roadways. |
| Survey Questionnaire | A method of collecting information by asking a set of predetermined questions in a prescribed order. |
| Terrestrial Lidar | Ground-based Lidar systems used for capturing geometric data for objects on the Earth’s surface. |
DOTs’ Policies and Standards Related to Lidar Data Collection and Maintenance
Some states have published guidelines or specifications associated with Lidar technology in their survey or design manuals. Table 2 provides a list of several state DOT Lidar manuals and links, offering direct access to these important documents. This table highlights guidelines and specifications that have been published by state DOTs, showcasing the widespread adoption and customization of Lidar technology across different states.
Table 2. State DOTs with Lidar manuals.
Report Organization
This synthesis report is organized into several chapters to provide a comprehensive understanding of the utilization and impact of Lidar technology in transportation infrastructure management.
Chapter 1: Introduction sets the foundation by discussing the motivations and objectives of the synthesis, introducing key concepts, and providing an overview of the report structure.
Chapter 2: Lidar Technology examines the technical aspects of Lidar systems, discussing components, platforms, data sources, and the life cycle of Lidar data, offering insights into the operational intricacies of this technology.
Chapter 3: Data covers the Lidar data life cycle, data management and governance practices, data formats, and data mining.
Chapter 4: Applications summarizes representative examples of the diverse usages of Lidar in transportation, including roadway projects; operations, maintenance, and safety; mapping; and emergency response.
Chapter 5: Survey Results provides the responses from a questionnaire distributed to state DOTs, providing a detailed account of current practices and experiences with Lidar technology.
Chapter 6: Case Examples features detailed narratives from selected DOTs, illustrating practical applications and challenges encountered in real-world settings.
Chapter 7: Overview, Findings, and Future Research concludes the report with a synthesis of key insights, experiences gained, and recommendations for future actions. It also identifies the main obstacles in implementing Lidar data and suggests areas for further research and improvement.
The main body of the synthesis is followed by a bibliography, a list of acronyms, and three appendices. Appendix A contains the questionnaire text distributed to 51 DOTs. Appendix B contains the detailed responses, and Appendix C provides the questions used in the case example interviews.
