Project

Airbus / Local Resilience Forum Sprint

Directed 12 researchers to deliver a situational awareness pilot for emergency responders inside a one-month sprint supporting a multi-million-pound grant bid.

  • Python
  • ML
  • GIS
  • Real-time Pipelines
  • Change Detection

Core problem

Local Resilience Forums needed a way to fuse static hazard data with live, on-the-ground signal during incidents — fast enough to be useful and credible enough to underpin a major grant application.

Architecture

Architected a dashboard integrating spatial risk-ratings for waste and fire hazards with ML-driven change-detection on satellite imagery. Engineered a real-time social media scraping pipeline to ingest, parse, and rank the severity of localised hazard reports during crisis events. Coordinated a cross-functional team of 12 across data, ML, and front-end roles.

Business impact

Delivered a working pilot inside a one-month window that supported a multi-million-pound grant application — demonstrating an ability to direct a sizeable team end-to-end against an immovable deadline.