Performing condition assessments and asset inventories for our world’s roads, signs, trees, and more is important for Public Works Departments (DPWs) and Departments of Transportation (DOTs) to ensure the safety and functionality of the built environment. However, traditional methods of data collection and analysis can be time-consuming and labor-intensive, leading to inaccurate or incomplete information. Fortunately, there is a solution to these challenges: the use of LiDAR and AI for automated and standardized data collection, analysis, and reporting.
LiDAR, or Light Detection and Ranging, is a technology that uses lasers to measure distances and create detailed 3D maps of the environment. Artificial intelligence (AI) algorithms can then be used to automatically analyze and classify the collected data. This combination of LiDAR and AI provides a rapid, accurate, and comprehensive solution for infrastructure asset management.
One of the key benefits of using LiDAR and AI for infrastructure asset management is the ability to standardize data across projects. By providing data in industry-standard formats, LiDAR and AI make it easy to integrate the collected data into existing software platforms, reducing the need for additional software or training. This can greatly improve the efficiency and effectiveness of engineering projects, as it allows DPWs and DOTs to easily compare and analyze data from different projects.
Standardizing data across projects with LiDAR and AI can also help to avoid costly mistakes and the need for manual data collection. Because LiDAR data can be processed as many times as needed once it is collected, DPWs and DOTs can ensure that the data is accurate and complete before making decisions or taking action. This can save time and resources that would otherwise be spent on manual data collection and verification.
To illustrate the benefits of using LiDAR and AI for data standardization, consider a project that requires pavement condition data, traffic sign locations, and tree data. Traditional methods of data collection and analysis would require multiple teams to spend weeks in the field manually collecting this data. This is not only time-consuming, but it also exposes the teams to potential safety hazards and weather-related challenges.
By using LiDAR and AI for data collection and analysis, teams can greatly reduce the time spent in the field. Mobile mapping using LiDAR can collect the required data in a fraction of the time, often cutting field time by up to 80%. The collected data is automatically processed and analyzed by AI algorithms, eliminating the need for manual data processing and saving even more time.
Furthermore, the results generated by the AI algorithms are standardized and can be easily integrated into existing software platforms. This ensures that the data is accurate, complete, and consistent across projects, allowing DPWs and DOTs to make informed and data-driven decisions.
In conclusion, the use of LiDAR and AI for data standardization in civil engineering provides numerous benefits. It allows for easy integration of data into existing software platforms, improving the efficiency and effectiveness of engineering projects. It also increases the accuracy and reliability of the data, avoiding costly mistakes and the need for manual data collection. If you are a Public Works or Department of Transportation Director, we strongly encourage you to learn more about the benefits of LiDAR and AI for data standardization. Contact Cyvl today to request a demo and see the difference for yourself.