AI-Ready Telecom Asset Annotation from Field Images

About the Customer

A US-based broadband SaaS provider offering a map-based platform for planning, building, and managing fiber optic networks.

Situation

The client relied on field photographs to document assets such as poles, cabinets, ducts, and fiber components. Asset identification from images was manual, inconsistent, and costly, leading to delayed audits, inaccurate inventories, and limited AI adoption.

Task

• Convert raw field images into a standardized, labelled dataset
• Define a consistent telecom asset taxonomy
• Deliver AI/ML-ready outputs for computer vision, GIS, audits, and digital twin systems

Action / Approach

• Used Label Studio for scalable, multi-reviewer annotation
• Defined clear asset classes: poles, cabinets, manholes, handholes, ducts, fiber indicators, and splice closures
• Applied bounding-box annotation for object-detection compatibility
• Followed a controlled workflow: annotation → review → correction → final export
• Delivered datasets in COCO, YOLO, and Pascal VOC formats with audit metadata

Result

• Reduce manual asset verification effort
• Improve inventory accuracy and consistency
• Accelerate computer-vision model development
• Enable GIS integration and digital-twin updates
• Lower operational costs and improve planning efficiency

For Information, please visit – Geospatial Data