Processing LiDAR at the Speed of Light

Teren delivers information in a fraction of the time using Leica HxMap and the Teren Engine on the cloud.

Note: This article was originally published as a case study by author Linda Duffy on Hexagon’s Leica Geosystems website. At that time, Teren was doing business as SolSpec.

LiDAR data can be too much of a good thing. The newest generation of airborne LiDAR sensors are capable of collecting very dense point clouds that contain critical information for many applications; however, processing and managing these enormous data sets is daunting for beginners and companies that lack the resources to invest in the required computing infrastructure. Long processing times can bog down the entire workflow and delay the most important step – analyzing the data and delivering actionable intelligence to the customer to enable decision making.

Aerial LiDAR data

Teren is a geospatial data collection, processing and analytics company with unique expertise in remote sensing applications of earth science. This Denver-based company started out offering drone acquisition and processing services, primarily to the oil and gas industry, but soon pivoted to a scalable aerial strategy serving a broad range of customers.

In 2019, Teren purchased a pair of Leica TerrainMapper linear-mode LiDAR sensors with built-in digital cameras that capture 4-band data (RGB and NIR) and cover more ground in a far shorter time than a drone. The aircraft-mounted system flies much higher and faster, efficiently collecting a broader area, and making it a scalable tool for diverse situations. On average, a single day of capture at 20 points per square metre (ppsm) produces 1 TB of highly accurate data.

“By collecting higher point densities with the TerrainMapper, we provide better data to our customers,” says Toby Kraft, Founder and CEO of Teren. “This translates into more valid analytics to support the decisions being made.”

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High-performance computing technology

After identifying the traditional processing workflow as an obstacle for expedited delivery of information based on aerial LiDAR data, Teren realised the cloud offered the opportunity to scale up multiple machines to process data in parallel. To harness the near-infinite computing power of the cloud, the innovative company built the Teren Engine on a high-performance computing and machine learning backbone.

Leica HxMap, a multi-sensor platform designed to leverage the benefits of the TerrainMapper data, has an open interface processing workflow that accommodates cloud processing, which is unique in the industry. HxMap can be installed from a single workstation to high end cluster environment with hundreds of nodes for high volume production. The workflow can be orchestrated in different engines using the many command line applications that can be scripted to fulfill various workflows to produce different output products.

When deployed on top of the Teren Engine in the Amazon Web Services (AWS) cloud, HxMap processes large volumes of aerial data in a compressed time frame. Deployment of HxMap into the AWS cloud on Linux allowed Teren to rapidly scale data processing capacity. The company increased its processing speed for large airborne LiDAR data sets by up to 60 times, which reduced post-processing costs and decreased the data delivery time to the customer. In addition, by building decision-ready analytics into the Teren Engine, Teren significantly reduced “time-to-decision” with LiDAR data.

“The flexibility that Hexagon has shown in embracing new technology and the willingness to work with us were main drivers for our partnership,” Kraft says. “We’ve automated the proprietary TerrainMapper format, taken core libraries and adapted them to run on top of a cloud engine. This structure allows us to go from raw wave-form data to a point cloud to a terrain model in a fraction of the time compared to conventional providers.”

Teren Geohazard Analytic is built on machine-learning and trained on 15,000 historic landslides to identify landslide signatures in a digital terrain model even in areas covered by vegetation. Courtesy Teren.

Real-life applications

One flagship product for Teren is Right-of-Way (ROW) Integrity Management. Pipeline operators frequently deal with environmental conditions like landslides and erosion that threaten to rupture pipes and interrupt service. If there is a thick tree canopy obscuring the slide activity, it is difficult to identify problems. The TerrainMapper effectively penetrates tree canopies to collect high density point clouds that contain a great level of detail. After processing data in the cloud, machine-learning algorithms are used to identify hazards through the canopy, alerting customers more quickly to potential issues.

Similarly, Teren’s vegetation management solutions are highly dependent on rich, high density point clouds combined with the high resolution 4-band spectral data from the TerrainMapper.

By leveraging spectral and structural data, Teren data scientists have developed algorithms that detect where vegetation is encroaching on powerlines, predict wildfire risk, identify wetlands, classify vegetation types for assessing reclamation activities, and many other applications.

“We have a perfect marriage between the data collection with the TerrainMapper and the processing with HxMap,” says Kraft. “This allows us to leverage the nearly unlimited computing power on AWS and deliver information incredibly faster.”

Teren’s Surface Hydrology Analytic models hydrologic energy and surface flow across high-resolution digital terrain models to accurately identify erosion issues. Courtesy Teren.

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Streamlining the LiDAR value chain

Teren’s ultimate goal is to transform aerial data into actionable risk analysis, decision support and predictive modeling. By combining aerial data and project-specific data with machine learning tools, Teren produces comprehensive reports about site conditions more quickly than other providers.

Approximately half of a LiDAR project’s cost is acquiring data, and the other half is processing. A conventional provider may take 30 hours to process 3 square miles of data (20 ppsm, QL1) in the office, while the Teren Engine does the same work in 20 minutes in the cloud. Processing with HxMap on the super-fast SolSpec Engine is a key competitive advantage to support time-sensitive decision making.

“Hexagon was an obvious choice for us as a partner because of its reputation in the aerial LiDAR industry,” continues Kraft. “Its sensors and software are very reliable and produce excellent results.”

The combined time savings of processing in the cloud and automating analytics with machine-learning algorithms increases the value of LiDAR data and expands its usefulness to a variety of markets.

“Teren helps its customers identify, measure, prioritize and monitor the most common environmental threats to their infrastructure on a recurring basis,” explains Kraft. “We streamline the entire LiDAR value chain by processing more quickly, managing the data, running the analytics, and providing the answers that customers need. This enables our customers to quickly make decisions about erosion, geohazards, vegetation, encroachment, structures, and more at scale.”

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