# Preprint automates geospatial forecasting from data selection through model training

_Published Monday, September 21, 2026 at 5:09 PM EDT · AI, Science · Latest · Tier 2 — Notable_

Researchers introduced the Planetary Prediction Engine, a preprint system that turns natural-language questions into geospatial forecasts by finding relevant open-web and Earth-observation data, combining it with foundation-model map embeddings and selecting a task-specific model with overfitting checks.

The authors report that the system raised mean R² across 21 U.S. health indicators from 60.0% to 76.8% and improved Nigerian food-security prediction from 31.5% to 66.1%. For weekly forecasts of the 2026 Democratic Republic of Congo Bundibugyo Ebola outbreak, it identified 15 of 18 newly affected health zones. These results are preprint claims across selected tasks and have not established performance in routine operational use.

## Sources

- [cs.AI updates on arXiv.org](https://arxiv.org/abs/2608.26088)

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Canonical: https://techandbusiness.org/newswire/duHrHaLnxQeqPVFPjbP0Nu
Published: 2026-09-21T21:09:33.104Z
Story chronology: 2026-09-21T04:00:00.000Z
Retrieved: 2026-09-21T23:02:19.900Z
Publisher: Tech & Business (techandbusiness.org)