NM Group researching machine learning for extracting complex powerline features from lidar

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NM Group, a provider of asset management, survey and mapping solutions for the power sector, is exploring the use of machine learning and deep learning approaches to improve automatic feature extraction from lidar data.

The R&D project will be undertaken as part of an ongoing relationship with Durham University, with a research associate working in NM Group for a year. That associate will complete academic research and, “crucially,” apply it in the industry-specific context of lidar data use for electrical networks.

“Automatic feature extraction has been around for a while,” explained senior client manager for NM Group Tim Hustwayte. The technology is currently capable of extracting information like the location of infrastructure, vegetation, buildings, and powerline assets. However, Hustwayte continunes, “we still have not seen an example that provides the quality and accuracy that we need to produce our service. Therefore, this project will be about leveraging the latest academic research to help deliver something new and practical.”

In an official statement, NM Group says that the research is intended to develop feature identification with high enough accuracy to “reliably identify complex powerline features in sufficient detail.”

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Sean Higgins is the former Managing Editor of SPAR3D.com and the SPAR 3D weekly newsletter, which continues to highlight relevant news and information for 3D-scanning professionals. Sean continues to work in the industry, and may sometimes be spotted in attendance at SPAR 3D Expo & Conference. Follow @spar_editor to connect with and learn more about the current voice of SPAR3D.com

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