All work

Machine Learning Engineer

ArborMeta

Byron Bay, NSW · 2025 – present

The geospatial ML platform behind Australian carbon-credit policy advice.

Problem

Carbon and ecology data was scattered across field surveys and raw LiDAR. None of it sat in one place a policymaker could actually look at, and none of it answered the question that matters: how much carbon, and is it growing?

Approach
  • Canopy-height and above-ground-carbon models from paired LiDAR captures, with growth quantified between flights parcel by parcel
  • Live fire-spread and ember mapping driven by wind fields
  • A multi-LOD PMTiles pipeline that keeps continental-scale datasets fast at every zoom level
  • A PostGIS spatial backend, with parsers that pull Fulcrum field surveys straight into the platform
  • A map viewer that renders live survey data over satellite imagery
Outcome
  • Used to advise federal and state government on carbon credit rules
  • Canopy growth and carbon change measured between repeat LiDAR captures, the numbers under the policy
  • Field data from four countries, readable in one live interface
Stack
FastAPIPostGISMapLibre GL JSPMTilesALS / LiDARDockerReact