Telecom Towers: From Drone Photos to Production AI on Digital Twins

Overview

We extended a telecom digital-twin platform so engineers could plan upgrades, verify leases, and keep documentation in sync. Over 12 months, we delivered equipment detection, real-world alignment, compliance checks, CAD/BIM outputs, and a hardened data connector.

At a glance
Industry

Telecom

Goal

Make the twin actionable (inventory, compliance, planning)

Tech highlights

Python, TensorFlow/Keras (YOLOv8), FastAPI, Angular, AWS/Terraform, Revit/AutoCAD APIs, GitLab CI

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Core Business Challenge

  • Imperfect 3D meshes from photogrammetry; no labeled inventory

  • Prototype → production gap (no tests/staging, brittle components)

  • Design tools as source of truth (Revit/AutoCAD had to stay authoritative)

Our Approach

  • Structured delivery: 2-week sprints, shared boards, weekly demos; clear Definition of Ready/Done.

  • Risk-down early: Short spikes on data quality, model choice, retrieval, and latency.

  • Operationalisation by default: Unit tests, GitLab CI, staging for the Smartsheets connector, runbooks and docs.

  • MLOps by default: dataset/model versioning, automated train/eval, testable APIs, and deployable components integrated with the client’s stack.

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AI Solution

  • Equipment detection: YOLOv8 on 2D mesh slices → 3D boxes via ray-casting (separate tower/ground models)

  • RTK alignment: transform from photogrammetry space → world coordinates

  • Compliance: “Vertical Space” checks devices vs. leased segments (RADs) with numeric/visual reports

  • CAD/BIM outputs: parametric Revit families, richer AutoCAD exports; Smartsheets connector stabilised

Outcome

  • F1 ↑ from 0.72 → 0.86 (tower-equipment model, retrained on latest YOLOv8)

  • Safer releases: staging + tests for the connector (no direct-to-prod)

  • Faster planning/reviews: parametric families and exports reduced redraws

  • Actionable compliance: automatic RAD violations with evidence for remediation

  • Clean handover: documented pipelines, dashboards, and runbooks

Client’s opinion
After an extensive search and in-depth reference checks, we chose Sparkbit to be our partner in solving previously unsolvable problems. They were able to provide us with AI and ML experts who grasped the complexities of our unique requirements and were able to deliver AI models. But they also found opportunities for us to reduce compute costs by identifying those areas where the answer was just math.
Anne ZinkCEO & Founder5x5 Technologies

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