AI-Driven Digital Twin Inspection System for Large-Scale Telecom Infrastructure

The client operates a vast infrastructure of over 100,000 cell towers nationwide, with manual inspection and maintenance processes leading to high operational costs, slow data processing times, and inconsistent data quality. Manual data annotation and postprocessing require significant human effort, limiting scalability and delaying network expansion efforts, especially in new markets like Europe.

A large telecom infrastructure company managing tens of thousands of cell towers across multiple regions, seeking to automate and digitalize tower inspections and asset management processes to improve scalability and operational efficiency.

• Develop an AI-powered system to automate the recognition and tagging of tower features, including installed equipment and available physical space, reducing analysis time by at least 60%.
• Implement a scalable architecture supporting rapid deployment and system development, facilitating easy addition of new features and expansion into new geographical markets.
• Establish reliable MLOps pipelines for efficient experimentation, monitoring, and deployment of machine learning models.
• Create an integrated digital twin platform accessible to asset managers for real-time asset monitoring, discrepancy detection, and visualization.
• Reduce operational costs associated with manual inspections and digital postprocessing by automating key system workflows.

• Automated generation of 3D digital twins from drone imagery using photogrammetry techniques.
• Machine learning models for 3D object identification, image recognition, and graph analysis to recognize tower components and free space.
• Automated annotation of tower features, including equipment installation and available space, with minimal manual input.
• Evaluation modules to compare designed versus recorded asset states, highlighting discrepancies.
• A user-friendly dashboard providing asset summaries, visualizations, and discrepancy reports.
• Version-controlled ML pipelines supporting experimentation, training, validation, and deployment workflows.

• Drone imaging data ingestion systems
• Photogrammetry and 3D modeling software for initial digital twin creation
• Asset management and asset database systems for synchronization
• Discrepancy detection and reporting modules
• End-user dashboards and visualization platforms

• System scalability to process and analyze data for over 100,000 towers efficiently
• Processing time reduction of at least 60% per asset analysis
• High system uptime and reliability, supporting continuous operation
• Data security and compliance, ensuring secure data transfer and storage
• Real-time monitoring and logging for ML experiment tracking and system health

The proposed system aims to significantly lower operational costs associated with manual inspections, accelerate digital asset assessments, and support rapid expansion into new markets. By automating tower feature recognition and discrepancy detection, the client can handle a larger asset portfolio efficiently, with an expected reduction in processing time by at least 60%, enabling scalable growth and improved asset oversight across regions.