Segment Solar Panels in Aerial Imagery for an Energy Audit Startup
Overview
What this challenge is about.
Segment Solar Panels in Aerial Imagery for an Energy Audit Startup. Intermediate challenge in code. Writing production code that solves real engineering prob...
The Brief
What you'll do, and what you'll demonstrate.
Triple annotator throughput on rooftop solar audits by pairing a segmentation model with a fast correction tool.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Train a semantic-segmentation model on aerial imagery
- Build a thin human-in-the-loop correction interface
- Evaluate segmentation with IoU on a clean held-out set
- Measure time-savings of a model-plus-human workflow against human-only
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Semantic Segmentation
Apply semantic segmentation to solve real industry problems and demonstrate production-level capability.
- U Net
Apply u net to solve real industry problems and demonstrate production-level capability.
- Aerial Imagery
Apply aerial imagery to solve real industry problems and demonstrate production-level capability.
- Human In The Loop
Apply human in the loop to solve real industry problems and demonstrate production-level capability.
- Streamlit
Apply streamlit to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Computer Vision Engineer
Shipping a segmentation model plus a correction tool on real aerial imagery is core CV-engineer work at climate, mapping, and insurance-tech startups.
This challenge sharpens
- semantic-segmentation
- u-net
- aerial-imagery
Machine Learning Engineer
Pairing a model with a human-in-the-loop tool plus an honest time-savings measurement mirrors what MLEs ship for ops teams.
This challenge sharpens
- human-in-the-loop
- evaluation
- semantic-segmentation
AI Engineer
Standing up the Streamlit correction tool plus a rollout plan is exactly the glue-engineering AI engineers do at vertical-AI startups.
This challenge sharpens
- streamlit
- human-in-the-loop
- evaluation