Accurate Asset Mapping:
‍Trained neural networks enable precise identification and mapping of assets, such as utility poles, telecom cabinets, and other visible network equipment, with high levels of accuracy. This ensures an up-to-date and comprehensive inventory of assets, facilitating efficient asset management and maintenance.

Operational Cost Reduction:
Traditional methods of manually inspecting and monitoring roadside assets are time-consuming and resource-intensive. Automation of this activity using computer vision significantly reduces operational costs by eliminating the need for manual surveys. It enables more efficient allocation of field resources, reducing labour costs and increasing overall productivity.


Enhanced Safety and Reliability:
By automatically monitoring roadside assets on a regular basis, maintenance teams can receive alerts about areas requiring infrastructure upgrades and address problems promptly thus minimising downtime and ensuring uninterrupted services.


Data-Driven Insights:
Computer vision technologies have the potential to generate vast amounts of data about roadside assets. By leveraging this data, telecom and utility operators can gain valuable insights into asset performance, trends, and patterns. This information helps in predictive maintenance planning, optimising resource allocation, and making informed decisions for future infrastructure development.