We can create a low cost digital twin of any network infrastructure automatically, using available imagery. Given a sufficient amount of representative training samples, any object that can be seen by the human eye can be detected using computer vision.  Three significant use case examples for asset mapping from street level imagery are outlined below and there are many more.



Telecoms & Utility Companies
Using computer vision and street-level imagery for mapping and monitoring of roadside assets offers unparalleled accuracy in asset mapping, operational cost reduction, enhanced safety, and data-driven decision-making for telecom and utility operators.
We estimate that 1 in 3 service calls do not result in work order €350-€600 per day, engineers’ time is a precious commodity.
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.
Road Management Agencies
Conducting automated surveys of road signs empowers local government and road management agencies with accurate, reliable, and up-to-date information about their roadside infrastructure. The benefits include comprehensive inventory management, improved accuracy and reliability, timely maintenance, enhanced road safety, regulatory compliance, cost efficiency, data-driven decision-making, and support for future planning and infrastructure development. The system can detect and classify various types of signs, including regulatory, warning, and information signs. This provides the operational teams with a comprehensive inventory of road signs, facilitating better management and maintenance thorough:
Accurate and reliable inventory:
Leveraging AI and computer vision reduces human error and improves data quality ensuring a high level of accuracy and reliability in identifying and analysing road signs.

Enhanced road safety:
Accurate and up-to-date information about road signs contributes to improved road safety giving insights into the condition and visibility of signs. This data can guide targeted interventions, reducing accidents and enhancing driver awareness.

Cost Efficiency:
Automating the survey process using AI and computer vision technology can significantly reduce costs compared to traditional manual surveys. It eliminates the need for extensive human resources, time-consuming fieldwork and data processing. The municipality can allocate resources more efficiently and prioritise maintenance and replacement efforts based on the survey results.
Smart Cities
Digital twins of the city infrastructure supports urban planning and development initiatives. By understanding the distribution and condition of road signs, traffic lights, storm drains etc., urban planners can make informed decisions about infrastructure expansion, redesign, and development to meet the area's evolving needs.

Accurate mapping also allows planners to model responses to environmental disasters such as flood and fire events, facilitating the coordination and optimal use of scarce resources to attend e.g. storm drains and fire hydrants using exact gps coordinates for navigation.

Traditional methods of infrastructure mapping can be time-consuming, costly, and prone to errors. Leveraging technology to analyse infrastructure remotely using street level imagery ensures a high level of data accuracy and currency, providing urban planners and other decision-makers with the most accurate, up to date information.

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Additional use case
examples include:
5G network planning at the pico cell level, identifying host infrastructure
Maps for autonomous driving
Environmental monitoring such as green space and invasive species

Case Studies

ai mapit has completed several successful case studies, across a variety of use case, industry and geography, as we developed out our technology and pipelines.
More Case studies
Smart Dublin (Docklands, 2022)
Enhancing Urban Efficiency and Safety: A Case Study of Smart Infrastructure Mapping in Dublin...
Telecoms Infrastructure (UK, 2020)
Rationale: to have a digital twin of all assets in the network for improved planning...
Telecoms Infrastructure (Ireland, 2017)
First prototype of asset detection and mapping of telegraph poles for eir...