Planetary prediction engine: Automating global models via Earth AI

Google Research introduced the planetary prediction engine (PPE), an experimental AI system that autonomously executes the full geospatial modeling workflow from data discovery to model training, reducing model-building time from weeks to minutes. In evaluations, PPE achieved a mean R² of 76.8% across 21 CDC health indicators versus 60.0% for a manual expert pipeline, and improved FEMA national risk and Social Vulnerability Index predictions. The system is part of Google Earth AI and aims to address challenges in public health, food security, environmental risk, and socioeconomics.

August 27, 2026 Rama Pasumarthi, Staff Software Engineer, and Shravya Shetty, Distinguished Engineer, Google Research As part of Google Earth AI https://blog.google/innovation-and-ai/products/google-earth-ai/ , we introduce the planetary prediction engine PPE , an experimental research capability that autonomously executes the full geospatial modeling workflow — from data discovery to model training — achieving improvements across diverse prediction tasks in public health, food security, environmental risk, and socioeconomics. Addressing humanity's most pressing global challenges — from forecasting regional food security and environmental disaster risks to tracking real-time disease outbreaks and mapping socio-economic vulnerability — requires high-fidelity geospatial modeling. However, building these models is hindered by a fragmented data ecosystem that requires specialized teams to spend weeks on manual data curation, feature engineering, and specialized spatial validation. While existing AutoML https://en.wikipedia.org/wiki/Automated machine learning and LLM-based agents effectively automate standard machine learning pipelines, they rely on pre-curated tabular data and lack the specialized capabilities needed to autonomously handle geospatial workflows. Consequently, planetary-scale analytics remains a significant bottleneck, a limitation that is especially severe when rapid response is critical during humanitarian crises. Today we introduce the planetary prediction engine http://arxiv.org/abs/2608.26088 PPE , the latest experimental research capability within our broader Google Earth AI https://ai.google/earth-ai/ initiative that turns planetary information into actionable insights. Previously, we demonstrated Earth AI’s reasoning capabilities https://research.google/blog/google-earth-ai-unlocking-geospatial-insights-with-foundation-models-and-cross-modal-reasoning/ in intelligently bringing together diverse geospatial assets. As an autonomous AI system, PPE executes the full geospatial prediction workflow, from planetary-scale data discovery and cleanup to training and evaluation, directly from natural-language queries. Given a geospatial predictive query, PPE will autonomously retrieve relevant data, engineer features, train and evaluate predictive models, and generate a comprehensive report. We demonstrate that by delivering improvements over diverse benchmarks without manual intervention, PPE effectively reduces the time needed to build complex planetary prediction models from weeks that include manual data engineering to mere minutes that result in autonomous insight. The PPE decomposes the predictive workflow into three modular stages, each orchestrated by an LLM: Critically, each stage operates independently on well-defined inputs and outputs to prevent data bottlenecks. Data artifacts are passed between stages via opaque handles rather than serialized into LLM prompts, avoiding context-window limitations. We evaluated the PPE across a multidimensional matrix of machine learning paradigms, geographies, and scientific domains. Across 21 CDC health indicators https://www.cdc.gov/cdi/indicator-definitions/index.html toc , the PPE’s intelligent data selection and multimodal fusion achieve a mean R² https://en.wikipedia.org/wiki/Coefficient of determination of 76.8% vs 60.0%, compared to a manual expert pipeline. We observe similar gains for predicting FEMA national risk indicators https://www.fema.gov/flood-maps/products-tools/national-risk-index mean R² of 64.9% vs. 60.0% baseline and Social Vulnerability Index https://www.atsdr.cdc.gov/place-health/php/svi/index.html mean R² of 66.2% vs. 58.6% baseline . In data-scarce regions, coarse regional reporting often obscures local vulnerability. By autonomously integrating localized market shocks, food price anomalies, and microclimate indicators, PPE doubles baseline accuracy when downscaling food security from the provincial state ADM1 level to the local government area https://en.wikipedia.org/wiki/Local government areas of Nigeria ADM2 level R² 66.1% vs. 31.5% , providing humanitarian organizations with actionable, high-fidelity vulnerability maps. For real-time prediction of new disease transmission hotspots during the 2026 Bundibugyo ebolavirus outbreak https://www.who.int/emergencies/disease-outbreak-news/item/2026-DON614 in the Democratic Republic of the Congo, the PPE achieves a Recall@10 https://en.wikipedia.org/wiki/Precision and recall of 83.3%, correctly identifying 15 of 18 newly invaded health zones across five sequential weekly forecasts. This represents a +10.3 percentage point absolute improvement over the published state-of-the-art Bayesian modeling baseline https://www.epidemiological.org/t/real-time-spatiotemporal-risk-modelling-of-the-bundibugyo-ebola-virus-outbreak-2026/16 ~73% , driven by fusing epidemiological signals with PDFM embeddings and PPE-selected geospatial covariates. A key finding across all experiments is the synergistic value of combining structured statistical covariates with latent foundation model embeddings. Neither modality alone captures the full picture: statistical covariates provide explicit, interpretable signals, while Population Dynamics Embeddings https://developers.google.com/maps/documentation/population-dynamics-insights/overview and AlphaEarth Foundations embeddings https://developers.google.com/earth-engine/datasets/catalog/GOOGLE SATELLITE EMBEDDING V1 ANNUAL encode complex non-linear patterns learned from large pre-training datasets. Our ablation studies consistently show that multimodal fusion along with intelligent data selection outperforms baseline approaches, confirming that these representations are complementary rather than redundant. In future work, we want to expand these capabilities to include more geospatial data sources, and foundation model embeddings such as Remote Sensing Foundations multimodal embeddings https://research.google/pubs/a-remote-sensing-vision-language-foundation-model-for-zero-shot-tasks/ . The PPE demonstrates that autonomous AI systems can match or improve performance across a wide range of geospatial prediction tasks: from estimating chronic disease prevalence in the United States, to improving resolution of food insecurity in Nigeria and nowcasting active viral outbreaks in the Democratic Republic of the Congo. By combining intelligent data discovery, multimodal foundation model fusion, and automated model optimization, the PPE lowers the technical barrier to planetary-scale analytics, enabling rapid deployment when time-sensitive decisions are critical. We believe this experimental research represents a meaningful step toward democratizing geospatial prediction. By shifting the focus from manual data engineering to high-level hypothesis direction, the planetary prediction engine helps researchers, humanitarian organizations, and policymakers build models without needing specialized engineering teams. While PPE is an early-stage research project, we’re excited to explore more use cases and see how this approach can help organizations better anticipate and solve complex global challenges. We thank and acknowledge the contributions from all of the co-authors of the paper. We are grateful to the UN World Food Programme WFP and Vulnerability Analysis and Mapping VAM team for the data and research support. We also thank the Institut National de Recherche Biomédicale INRB for their collaboration on the DRC Ebola nowcasting, and the teams behind Data Commons, Google Earth Engine, Population Dynamics Foundation Models, and AlphaEarth for providing the foundational data and model infrastructure that power the PPE.