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.
Architecture
Engineered a semantic segmentation pipeline using GEOSAM to process high-resolution SPOT/Pléiades satellite imagery for granular Grey Belt land identification. Managed GCP and Google Workspace cloud environments for the cohort, standardising shared infrastructure so the team could focus on modelling rather than ops. Delivered the final technical presentation directly to Knight Frank stakeholders.
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.