Harris offers high-altitude, high-precision Geiger-mode LiDAR data that can map an entire network at the highest point densities to extract intricate details of network assets, providing a complete and accurate record of the as-built T&D infrastructure. Since data are collected as wide area coverages, other critical information can be obtained in contrast to typical ROW-only collection approaches. This unique technology creates point clouds that support derivative products for a variety of utility applications and subsequent analytics across all parts of the organization.

Harris utility offerings include deploying any number of analytics, including image classification, multi and hyperspectral analysis, and LiDAR feature extraction. These capabilities are brought together in the form of utility-specific workflows, allowing a utility of any size to take advantage of all of the power of Harris’ remote sensing analytics without requiring a staff of image scientists.

Over time as more data is collected along the network, Harris’ change detection capability can be utilized for any time period, providing proactive insights on changes occurring on the network, such as increasing pole tilt with time, changes in joint use attachments, or increasing amounts of rust on equipment.

Harris has applied deep learning technology to automate a wide range of utility asset management problems, and has deployed its advanced deep learning technology, MEGA, in various utility scenarios including asset location and identification, anomaly detection, and monitoring change over time. In addition to standard libraries designed to detect common anomalies on T&D infrastructure, new classifiers can be developed with specific data collected by the utility and deployed to provide tailored inspection analytics. These analytics can be automated to run on data ingest in real-time, or used interactively by analysts to review and improve the deep learning models.

Harris’ deep learning technology can detect anomalies on assets such as: missing or damaged components, pole split/rot, bird’s nests or other animal infestation, lightning strikes, corrosion, or rust. Using infrared data, analyses of temperature profiles on equipment can be automatically generated to provide insights on power flow and predict potential equipment failures.