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Digitizing Grid Infrastructure – IEL Report

Central Hudson Gas & Electric partnered with Looq to to rapidly capture, create, and analyze a large, representative data set of distribution poles, lines, and substations including creation of 3D point clouds; panoramas; ortho-images (nadir view); segmentations of lines, poles, vegetation, ground and other features; and extraction of line-pole geometry for loading analysis.

Final Report: Looq AI and Central Hudson Gas and Electric As published for Incubatenergy Labs

Technology Solution

Established in 2021, Looq AI is a survey technology platform company dedicated to advancing critical infrastructure digitization and analysis. Developed by a team of scientists from UC San Diego, the company’s 3D-AI software technology cuts field survey times by over 10x enables the rapid and scalable 3D capture of survey-grade information for topographic mapping and utility assets for data-driven decisions and improved operational efficiency.

The Looq AI platform (Fig. 1) includes a qCam handheld multicamera and GPS data capture device, a cloud-based processing service (qAI), and web-based application (qApp) for data visualization, analysis, and export. Creating accurate 3D digital twins of distribution infrastructure is as simple as walking around the poles and lines of interest with the qCam, uploading the data, and visualizing and analyzing the models on qApp 24 hours later (Figs. 2-5).

Figure 1: The Looq AI Platform: qCam handheld multi camera and GPS data capture device; qAI cloud-based processing service; and qApp web-based application for data visualization, analysis, and export.

Figure 2: Project coverage area, sample of captures and workflows.

Figure 3: Looq Platform's qApp provides access to view, analyze, measure, collaborate, and extract data—view of measurements on distribution.


Figure 4: Utility vegetation management (UVM) is a challenging task, requiring management of tree and vegetation growth around power lines and utility assets to prevent outages and maintain consistent service.


Figure 5: Enhanced 3D visualizations of selected segments of the CHG&E distribution infrastructure.

Project Overview

The Looq AI and CHG&E project captured and analyzed a representative data set of distribution poles, lines, and substations to assess the Looq AI platform for usability, access to difficult locations, and analyses for pole loading and vegetation management. Deliverables included field data collection; 3D point clouds, panoramas, and ortho-images; segmentations of lines, poles, vegetation, and other features; and extraction of line-pole geometries of ~2500 poles and 5 substations.

Results and Learnings

  1. A single part time field person was able to capture all the data for the project; we estimate that dedicated personnel could capture 250 poles/day/person.
  2. With few exceptions, we were able to capture the many poles and lines on private property.
  3. The extremely densely foliated region of the Hudson Valley challenges the current version of our automated segmentation and classification algorithms.
  4. Large value lies in the ability to deliver automated defect detection in distribution poles for which we have the data.

“While we are only at the capture stage of this project, ultimately where we would like to be is delivering greater resilience in the grid – where customer interruptions from our overhead system due to equipment faults and vegetation impacts are rare and quickly addressed," says Chris Gilbert, Manager of R&D and Innovation. "With enhanced access to accurate, up-to-date data, we would be able to more proactively manage potential risks, optimize maintenance, and bolster system reliability. This approach builds on our existing strengths to deliver an even higher standard of dependable service that our customers can continue to count on.”

Sara Chilcott, Grants Coordinator /2024 IEL Project Manager says “Our partnership with Looq AI has been a collaborative journey, providing invaluable learnings for both sides. Looq’s openness to feedback has been instrumental in refining their technology to align with our specific needs and improving our workflows. Their commitment to innovation and responsiveness has strengthened our ability to take meaningful steps toward achieving our ideal state of a more resilient grid. As we continue this project, we’re excited to not only advance our own goals but also share these lessons learned to help other utilities implement similar solutions, driving progress across the industry.”

Implications and Next Steps

Large scale data capture and automated 3D intelligent model creation is now easily in reach for electric utilities seeking to improve documentation of their distribution and substation assets. Improved segmentation, classification, and defect detection is key to creating additional value for maintenance and vegetation management applications. CHG&E and Looq have agreed to continue working together to develop these capabilities, which will leverage the data collected and the learnings derived from this project.

“Working with the team at Central Hudson, Looq was able to understand their pressing needs in defect detection and vegetation management in addition to the data capture and geometric analyses that were the focus of the project," says Todd Hylton, VP Strategy, Looq AI. "Our work together continues as we develop these new capabilities in pursuit of a more reliable and more affordable grid. The fundamental need is unchanged, we need detailed, comprehensive information about complex and diverse infrastructure that is affordable and easy to deploy and adopt. The road ahead is challenging, but the path is clear.”

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