Planetary prediction engine: Automating global models

What happened

As part of 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.

9% vs. 2% vs. 6% baseline). In data-scarce regions, coarse regional reporting often obscures local vulnerability. 1% vs. 5%), providing humanitarian organizations with actionable, high-fidelity vulnerability maps.

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.

Why it matters

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 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 (PPE), the latest experimental research capability within our broader Google Earth AI initiative that turns planetary information into actionable insights. Previously, we demonstrated Earth AI’s reasoning capabilities 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. 0%, compared to a manual expert pipeline.

What to watch

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.