一句自然語言查詢跑完全套地理建模

Computer Science > Artificial Intelligence arXiv:2608.

26088 (cs) [Submitted on 26 Aug 2026 (v1), last revised 24 Sep 2026 (this version, v3)] Title:Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings Authors:Evelyn Ma, Rama Kumar Pasumarthi, Kishwar Shafin, Mandar Sharma, Mimi Sun, Hamed Sadeghi, Dav M.

Ebengo, Mbulayi Onesime, Ciara Judge, Rouslan Solomakhin, John Wamburu, William Ogallo, Aisha Walcott-Bryant, Sanxing Chen, Arbaaz Muslim, Yael Mayer, Ronald Ho, Roy Lee, Ruth Alcantara, Abdoulaye Diack, Monica Bharel, Lambert Rosique, Jeremy Amez-Droz, Christopher Haire, James Manyika, Yossi Matias, Niv Efron, Gautam Prasad, Shravya Shetty View a PDF of the paper titled Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings, by Evelyn Ma and 27 other authors View PDF HTML (experimental) Abstract:Addressing critical global challenges, from food security and disaster risk to disease outbreaks and socio-economic vulnerability, demands high-fidelity geospatial modeling.

However, building predictive planetary models remains bottlenecked by a fragmented data ecosystem, requiring manual data retrieval, multimodal data curation and fusion along with iterative model selection.

We present the Planetary Prediction Engine (PPE), an autonomous AI system that executes this end-to-end workflow directly from natural-language queries.

PPE synthesizes multimodal datasets on the fly, retrieving spatiotemporally relevant covariates across open-web and Earth observation platforms (Data Commons, Google Earth Engine) and fusing them with geospatial foundation model embeddings (PDFM, AlphaEarth).

Simultaneously, it searches over task-tailored model architecture families with automated overfitting guards.Across diverse tasks, geographies, and scientific domains, PPE consistently outperforms state-of-the-art or manually tuned expert baselines.

For US spatial regression, PPE improves mean $R^2$ across 21 CDC health indicators (76.8% vs.60.0%), FEMA national risk indices (64.9% vs.60.0%), and the Social Vulnerability Index (66.2% vs.58.6%).

For spatial downscaling in data-scarce settings, PPE integrates localized proxies to double baseline accuracy in Nigerian food security indicators ($R^2$ of 66.1% vs.31.5%).For epidemiological nowcasting of the 2026 DRC Bundibugyo Ebola outbreak, PPE achieves a Recall@10 of 83.

3% (identifying 15 of 18 newly invaded health zones across five weekly forecasts), a +10.3 percentage-point improvement over the public state-of-the-art modeling (~73%).

By combining autonomous multimodal planetary data discovery with targeted model optimization, PPE lowers the technical barrier to planetary-scale analytics, enabling rapid, customized, expert-level deployment.Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2608.

26088 [cs.AI] (or arXiv:2608.26088v3 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.

26088 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Rama Kumar Pasumarthi [view email] [v1] Wed, 26 Aug 2026 17:50:52 UTC (2,029 KB) [v2] Fri, 18 Sep 2026 16:19:48 UTC (1,655 KB) [v3] Thu, 24 Sep 2026 22:41:43 UTC (1,655 KB) Full-text links: Access Paper: View a PDF of the paper titled Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings, by Evelyn Ma and 27 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.

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