GE Digital Use Case

GE Digital and Alteia partner to bring visual intelligence to power and utilities.

GE Digital and Alteia have partnered to develop a Visual Data Management Platform that embraces new remote sensing technology and AI in order to offer utilities a clear path forward from their legacy inspection and risk management programs; unleashing the power of the (currently underused) data collected on their network, and offering them a single source of truth across their entire portfolio of assets in order to build predictive models and adopt a condition-based approach for their operations.

The platform provides several applications such as Vegetation Management and Asset Defect Recognition. These different applications are intended for use by Asset Managers and Vegetation Managers which addresses all potential risks arising on the network.  It improves existing knowledge and capabilities by giving teams the tools they need to make data driven decisions that combined with their own experience will reduce the operational costs of inspections while improving the decisions on asset and vegetation management and improving reliability and safety of your network.

The value proposition

Vegetation Management is a very serious challenge for utilities, which can be labor expensive. Trees that are too close to power lines are a significant hazard and a major cause of power failures during high winds and storms. In dry weather conditions, branches touching the lines can catch fire, endangering human life and causing tremendous damage to the environment and existing infrastructures. Dying trees falling over powerlines can have disastrous consequences by creating massive outages.

The management of vegetation on transmission and distribution corridors is key to provide a reliable supply of electricity and to ensure public and worker safety. Traditional approaches to Asset Inspection and Vegetation Management are slow and often based on Fixed Annual Cycles. Visual inspections generate large amounts of data which are often reviewed manually, which is labor and time expensive.

Measuring electrical corridor trees incursions and clearances with LiDAR mounted on helicopters or similar is standard industry practice, but how do you turn this data into insights to improve reliability and safety with less cost? In today’s digital world, predictive analytic technology certainly has a part to play especially within the world’s fastest moving digital utilities as they drive the direction of the digital utility of tomorrow. 

Predictive model of vegetation growth
Predictive model of vegetation growth
Predictive model of vegetation growth


Benefits of the solution

  • Know where your assets truly are: automated digitization of transmission and distribution networks (poles and conductors) and automatic synch with the GIS database
  • Reduce operational costs: Improvement of the Vegetation Management process by providing quicker and better results and keeping or reducing current OPEX spent on Vegetation Management programs;
  • Deploy a path towards predictive analysis: Condition-based veg management via the development of growth patterns and dynamic sag & sway models;
  • Assess your risk in real time for better planification: Risk measurement and optimization of work orders directly pushed into management systems;
  • Get an accurate view on your asset health and related maintenance costs: list of defective assets, including the type of defect and the location and automatic upload in GE asset management software;
  • Understand precisely the impact on the close environment: fire hazard risk mitigation by spotting dead or dying trees using multispectral or hyperspectral imagery