Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings
- Published
- Source
- arXiv
- Paper number
- 1031
- Field
- AI / General
- arXiv ID
- 2608.26088
Key points
- Using only a natural-language question, it automated a geospatial prediction pipeline of more than 700 steps, from data discovery to model selection.
- It builds predictive features by combining geospatial foundation-model embeddings with covariates collected in real time.
- It raised average R² across 21 US CDC health indicators from 60.0% to 76.8%, substantially outperforming the expert baseline.
- In forecasting the 2026 Ebola outbreak in Congo, forecasts over 5 consecutive weeks anticipated 15 of the 18 newly affected health zones (Recall@10 83.3%).
- It embedded validation mechanisms such as target-leakage prevention and overfitting guards within the agent loop.
Paper links
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