Build a Robust Image Classifier for a Climate-Tech Satellite Startup
Overview
What this challenge is about.
Train a ResNet-50 on satellite data to detect illegal construction using focal loss and test-time augmentation, then earn a verifiable certificate.
The scenario
The startup (15 people, Series Seed) screens roughly 40,000 square kilometers of coastline weekly; missed positives cost the NGO real legal follow-up time, so recall on the positive class matters more than accuracy.
The Brief
What you'll do, and what you'll demonstrate.
Retrain the coastal-construction classifier so per-shift recall on positives crosses 80% without crashing overall precision.
Earning criteria — what you'll demonstrate
- Apply modern data augmentation and regularization to a real classification task
- Handle class imbalance with appropriate loss functions and sampling
- Measure distribution-shift robustness explicitly, not just headline accuracy
- Deliver a model + report a working ML team can actually adopt
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Advanced Deep Learning
Master · Deep Learning
Strong alignment
This challenge maps to Advanced Deep Learning at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Data Augmentation
Apply data augmentation to solve real industry problems and demonstrate production-level capability.
- Deep Learning
Design and train neural networks for complex pattern recognition tasks.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Class Imbalance
Apply class imbalance to solve real industry problems and demonstrate production-level capability.
- Robustness
Apply robustness 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:
Machine Learning Engineer
Improving a production classifier on real distribution shifts and shipping a drop-in inference script is the day-one work of a junior MLE on any applied team.
This challenge sharpens
- pytorch
- model-evaluation
- data-augmentation
Computer Vision Engineer
Satellite-imagery classification with seasonal robustness is a clean CV-engineer skill bridge; the augmentation recipes transfer directly.
This challenge sharpens
- data-augmentation
- deep-learning
- robustness