Photo-based distance and setback estimation for pole replacement planning - BC-1039

Project type: Research
Desired discipline(s): Engineering - civil, Engineering, Computer science, Mathematical Sciences, Mathematics
Company: Anonymous
Project Length: 4 to 6 months
Preferred start date: 11/02/2026
Language requirement: English
Location(s): Richmond / Vancouver, BC, Canada
No. of positions: 1
Desired education level: Master'sPhD
Open to applicants registered at an institution outside of Canada: No

About the company: 

The partner organization (ANONYMOUS) is an established Canadian electrical utility consulting firm based in British Columbia, with a team of approximately 95 professionals. The company provides engineering services to major Canadian utilities across transmission, distribution, substation, and telecommunications infrastructure. A significant part of its practice is distribution pole replacement design: assessing end-of-life wood poles and producing the engineering design required to replace them safely and to standard. The company is investing in an applied AI/R&D program that uses computer vision and machine learning to automate the interpretation of field photographs of utility poles, so that engineering design information currently extracted manually can be pre-filled automatically with calibrated confidence scores and clear flags for designer review. Interns joining this program work with a real production dataset of field imagery and design records, alongside the company's internal ML, platform, and engineering teams, with a direct path from research results to deployment in a production design workflow.

Describe the project.: 

What is the project about / main goal: This project explores whether field photos can provide useful distance estimates for pole-replacement planning — anchor lead to pole, stake to existing pole, and stake to proposed new pole location — where manual measurements are currently required, with confidence scoring and a "manual measurement required" fallback for unreliable cases.
Main tasks to be performed by the candidate:
• Detect and localize existing pole base, anchors, guy wires, survey stakes, proposed pole markers, and curb/sidewalk/road reference features
• Evaluate monocular depth estimation, geometric calibration from known dimensions, and multi-photo triangulation where metadata is available
• Develop confidence rules separating usable estimates from cases requiring manual survey input
• Validate image-derived estimates against measured ground-truth distances from field records; stretch: estimate direction and offset of the proposed new pole location
Methodology/techniques to be used: Geometry-aware computer vision and photogrammetry; targets: median absolute distance error ≤ 0.5 m for clear cases, ≥ 80% of unreliable cases correctly flagged for manual review.

Required expertise/skills: 

Photogrammetry; monocular depth estimation; geometry-aware computer vision; practical ML systems.
Optional: assets
Production dataset of utility-pole field imagery with linked engineering design records; internal labeling platform and labeling pipeline; existing trained baseline models to build on; cloud GPU compute; day-to-day co-supervision by the company's internal ML and platform team.