Algorithmic Sustainability: Satellite Foundation Models in EUDR Compliance
- •Traditional pixel-classification baselines yield low recall (0.23) on dense tropical agricultural plots due to persistent cloud cover and canopy noise.
- •Foundation model geospatial embeddings capture multi-temporal contextual shifts across synthetic aperture radar (SAR) and optical imagery.
- •Regulatory compliance is permanently shifting from self-attestation disclosures to continuous satellite verification.
The European Union Deforestation Regulation (EUDR) represents a watershed moment for international trade. Rather than allowing companies to rely on vague supply-chain pledges, it requires plot-level coordinates demonstrating zero deforestation post-December 2020 for agricultural commodities entering Europe.
For global agribusinesses buying from millions of smallholders, ground surveys are impossible. Yet standard satellite heuristics struggle with tropical reality: persistent monsoon cloud cover, canopy shadow variations, and seasonal defoliation routinely trigger false alarms or miss clearing.
In our work for Plot Passport, submitted to Google DeepMind’s AI for the Planet APAC initiative, we tackled this with geospatial foundation models. AlphaEarth embeddings represent multi-temporal radar and optical observations as unified structural vectors rather than individual color pixels.
By anchoring these representations against Hansen forest loss records and JRC canopy baselines, we achieved near-perfect verification recall. The broader signal: sustainability compliance is no longer an exercise in corporate marketing; it is becoming an algorithmic discipline.
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