Data & Research/2026/Prototype
Odum — Cocoa Deforestation Traceability
Helps cocoa exporters prove their beans are deforestation-free under the EU Deforestation Regulation. It reads Sentinel-2 satellite imagery to catch post-2020 forest loss at sub-hectare resolution, which is exactly where the usual 30 m datasets tend to miss smallholder farms, then produces a due-diligence statement with the evidence attached.
Context
Under the EU Deforestation Regulation, a cocoa exporter has to prove its beans are not tied to land cleared after December 2020. The catch is that the 30 m forest-loss datasets most tools lean on quietly miss the small farms that make up much of Ghana’s cocoa, so exporters are left without a defensible answer for regulators.
Approach
- 01Built an offline machine-learning pipeline that ingests Sentinel-2 satellite imagery and detects post-2020 forest loss at sub-hectare resolution using a LightGBM change-detection model over weak labels.
- 02Separated the system into an offline analysis stage and a read-only online serving layer (FastAPI) so heavy geospatial compute never blocks the dashboard.
- 03Stored plots and verdicts in PostgreSQL/PostGIS and surfaced them through a Next.js + MapLibre console with per-district evidence tiles.
- 04Generated a citation-anchored EUDR Due Diligence Statement as a signed PDF, tracing each verdict back to its imagery evidence.
Notable
- Detects loss on plots that 30 m global datasets miss, closing the smallholder gap.
- Multi-district console with per-plot verdicts and drill-down evidence tiles.
- Regulator-ready, signed due-diligence output with source citations.
Next project
Sway — Influence Analytics Platform