CAEE students recently witnessed the collection of LiDAR data on UT Austin’s campus as part of a new student-accessible database called Digital Campus. Roughly 160 billion points on campus were acquired by a collection vehicle and will be developed into a refined dataset for further analysis by students.
LiDAR mapping systems, or high-resolution topography, and their enabling technologies allow engineers and scientists to examine natural and built environments across a wide range of scales with greater accuracy, precision and flexibility.
Members of the UT ASCE student chapter learned about LiDAR collection firsthand from specialists at Mandli Communications and Virtual Geomatics.
The Mandli team shared its state-of-the-art mobile platform for data collection, which creates an accurate three-dimensional model of a scanned environment through a single pass of their vehicle. It collects up to 1.4 million points of data per second at highway speeds, at ranges over 100 meters.
Students learned about the technology that is used inside and outside of the vehicle. Mandli currently uses the laser crack measurement system (LCMS) for 3D pavement profiling applications. This system is comprised of high-speed cameras, custom optics, and laser line projectors to acquire high-resolution profiles and images of the road. Acquired details of the road surface allow for the automatic detection of cracks and the evaluation of macro-texture and other road surface features.
After the LiDAR mapping was complete, Virtual Geomatics took the raw data and developed a point cloud, which represents three-dimensional representations of the Earth’s surface and its features. The point cloud can be utilized to take 3D measurements of roadside assets, including width, height, and length, surpassing the measurement capabilities of 2D images.
In fact, there are many potential applications for the dataset. CAEE Assistant Professor Paola Passalacqua and Associate Professor Ben Hodges are eager to see how the information can be used for improving modeling of urban flood flows and street drainage.
“With this type of dataset, we can extract preferential flow paths for severe storms and develop more effective models of street flooding,” says Passalacqua.
Other applications include updating and creating flood insurance maps, forest and tree studies, and coastal change mapping.
Students also learned about laser scanning accuracy and the amount of data collected compared with other survey collection systems. As a learning tool, LiDAR helps students better visualize and understand geoscience concepts.
