Project
Knight Frank Challenge — Grey Belt Classification
Technical lead for a 20-person PhD cohort delivering a national housing submarket classification project for Knight Frank, using semantic segmentation on satellite imagery.
- GEOSAM
- GCP
- Python
- Remote Sensing
- Semantic Segmentation
Core problem
Identifying Grey Belt land at national scale requires fine-grained classification of land use that is not captured in existing administrative datasets. Knight Frank needed a defensible, reproducible pipeline that could be rerun as imagery refreshed.
Business impact
Coordinated a 20-person research team to deliver a credible classification pipeline to a major property consultancy within a single challenge week — translating PhD-level methods into a stakeholder-ready output.
Summary presentation available at the link below.