以光速处理激光雷达

Author: Linda Duffy

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 – analysing the data and delivering actionable intelligence to the customer to enable decision making.

Aerial LiDAR data

SolSpec是一家地理空间数据收集，处理和分析公司，在地球科学的遥感应用中具有独特的专业知识。这家总部位于丹佛的公司开始提供无人机收购和处理服务，主要是向石油和天然气行业提供的，但很快就涉及可扩展的航空战略，为广泛的客户提供服务。

In 2019, SolSpec purchased a pair ofLeica TerrainMapper线性模式激光雷达传感器内置数字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 Seth Tribbey, VP of Sales and Marketing at SolSpec. “This translates into more valid analytics to support the decisions being made.”

High-performance computing technology

After identifying the traditional processing workflow as an obstacle for expedited delivery of information based on aerial LiDAR data, SolSpec 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 SolSpec 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 SolSpec 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 SolSpec 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 SolSpec Engine, SolSpec 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,” Tribbey 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.”

SolSpec 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 SolSpec.

现实生活中的应用

One flagship product for SolSpec 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.

同样，SOLSPEC的植被管理解决方案高度依赖于富的高密度点云以及TerrainMapper的高分辨率4波段光谱数据。通过利用光谱和结构数据，SOLSPEC数据科学家开发了算法，这些算法检测植被侵占电力线的位置，预测野火风险，识别湿地，对植被类型进行分类用于评估开垦活动以及许多其他应用。

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

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

Streamlining the LiDAR value chain

SOLSPEC的最终目标是将空中数据转换为可行的风险分析，决策支持和预测建模。通过将航空数据和特定于项目的数据与机器学习工具相结合，SOLSPEC比其他提供商更快地生成有关现场状况的全面报告。

LiDar项目成本的大约一半是获取数据，而另一半正在处理。传统的提供商可能需要30小时来处理办公室中3平方英里的数据（20 ppsm，QL1），而Solspec发动机在20分钟内在云中进行相同的工作。使用HXMAP在Super-Fast Solspec发动机上处理是支持时间敏感决策的关键竞争优势。

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

在云中加工的综合时间节省和使用机器学习算法自动化分析的时间增加了LiDAR数据的价值，并将其实用性扩展到各个市场。

Tribbey解释说：“ SOLSPEC帮助其客户识别，测量，优先级和监视对其基础设施最常见的环境威胁。”“我们通过更快地处理，管理数据，运行分析并提供客户需要的答案来简化整个LIDAR值链。这使我们的客户能够迅速做出有关侵蚀，地球岩，植被，侵占，结构等的决定。”