Earth AI: Unified Geospatial Intelligence

• Earth AI is a comprehensive geospatial intelligence framework that integrates multimodal foundation models to transform raw data into actionable planetary insights.
• It employs specialized models for remote sensing, population dynamics, and weather forecasting, achieving precision metrics like top-1 accuracy and R² improvements.
• A Gemini-powered agent orchestrates multi-domain reasoning, enabling transparent, benchmark-driven crisis management and sustainability assessments.

Earth AI refers to a comprehensive geospatial artificial intelligence framework that advances the analysis, understanding, and actionable inference of planetary processes by leveraging large-scale, multimodal foundation models, cross-domain data integration, and advanced agentic reasoning. It targets the central challenges associated with the volume, diversity, and complexity of modern geospatial data, providing a pathway from raw data to critical insights for applications in environmental monitoring, public safety, and global sustainability (Bell et al., 21 Oct 2025).

1. Foundation Models for Geospatial Domains

The Earth AI approach employs distinct, specialized foundation models across three essential domains:

• Planet-scale Imagery ("Remote Sensing Foundations"): These models consume a wide array of satellite, aerial, and ground imagery. Architecturally, they are based on vision–language frameworks such as RS-MaMMUT and RS-SigLIP2, which learn shared embedding spaces for images and text. This supports zero-shot classification, open-vocabulary object detection, and robust cross-modal retrieval. The core imaging backbone typically consists of massive vision transformers pretrained with self-supervised objectives (e.g., masked autoencoding) and multi-task learning on hundreds of millions of geotagged images.
• Population ("Population Dynamics Foundations"): This domain integrates population, mobility, and environmental signals into privacy-preserving digital region-level embeddings. These feature aggregations combine sources including digital maps, search activity, busyness signals, and physical environmental attributes. Embeddings are made available as both static (cross-region) and dynamic (temporal) representations to support interpolation and forecasting tasks at administrative-unit granularity.
• Environment (Weather and Climate Models): This suite encompasses operational numerical weather prediction models (e.g., MetNet), real-time flood forecasting via API, and experimental stochastic cyclone models. These components yield real-time and forecasted environmental variables, which are critical for downstream risk and impact assessments.

The foundation models are empirically validated on a range of public benchmarks. Remote Sensing Foundations are evaluated via top-1 accuracy, mAP (mean average precision), and text-to-image retrieval, while Population Dynamics Foundations use coefficient of determination (R²) for tasks such as night-time lights or population density estimation.

2. Gemini-powered Geospatial Reasoning Agent

Central to Earth AI is a Gemini-powered reasoning agent that orchestrates and integrates the outputs of these foundation models:

• Integrated Multimodal Reasoning: The agent accepts complex natural language queries and decomposes them into a sequence of actionable sub-tasks across domains. For example, it can answer queries such as "Which regions have both high flood risk and large vulnerable populations?" by invoking appropriate imagery, population, and environmental models in combination.
• Query Decomposition and Planning: The system internally plans multi-step reasoning pipelines, dynamically selecting which foundation models, APIs (e.g., Google Earth Engine, Maps), or analytical modules to invoke, ensuring compositionality in cross-domain inference.
• Transparent Execution and Auditing: Each step of the agent's reasoning, from tool invocation to data integration and final synthesis, is output alongside results. This transparency enables error tracing and interpretability in critical scenarios.
• Benchmark-driven Optimization: The agent is benchmarked on a suite of structured Q&A and crisis-response scenarios. Scoring is performed with automated metrics (e.g., ROUGE-L for textual output; clamped percent error for numerical answers) and reveals large improvements over baseline LLM agents, up to 64% in composite scores for complex multi-domain tasks.

3. Evaluation Benchmarks and Novel Capabilities

Earth AI foundation models and the corresponding Gemini-powered agent are validated against rigorous, multi-domain benchmarks:

• Vision–LLM Benchmarks: Zero-shot image classification, open-vocabulary object detection, and text–image retrieval across datasets such as FMoW, FLAIR, DIOR, and DOTA with clear performance metrics (Top-1 accuracy, mAP).
• Population Dynamics Forecasting and Interpolation: R² metrics for population, socioeconomic, and environmental variables, validating the interpretive and predictive strength of the embeddings.
• Multi-model Synergy: Joint inference scenarios (e.g., prediction of FEMA risk scores using both AlphaEarth imagery and Population Dynamics embeddings) demonstrate that multi-source integration yields an average R² improvement of 11% compared to single-source solutions.

4. Real-World Crisis Applications

Earth AI demonstrates successful application in operational crisis scenarios:

• Flood and Hurricane Damage Risk: By fusing real-time meteorological forecasts with physical landscape embeddings and population vulnerability features, the agent produces granular risk maps. During hurricane landfall case studies, it delivered damage forecasts within a three-day window and maintained predictive error margins of only a few percent.
• Public Health Prediction: The framework combines health event data (e.g., ER visits for infectious diseases) with environmental and mobility signals, supporting forecasts for flu, COVID-19, RSV, and cholera prevalence. Temporal population embeddings augment the system’s ability to anticipate spatial–temporal surges in risk.

5. Synergistic Model Integration and Predictive Improvement

A core scientific discovery is that the integrated use of multiple foundation models and advanced agentic reasoning leads to superior performance over any isolated model:

Foundation Model(s)	Task Type	Metric (Mean)	Synergistic Gain
AlphaEarth only	Risk Score Prediction	R²	Baseline
Population only	Risk Score Prediction	R²	Baseline
AlphaEarth + PopDyn	Risk Score Prediction	R² (avg. +11%)	+11%

This table illustrates that blended foundation model features (columns) enhance overall predictive accuracy for downstream applications (rows), such as FEMA risk scores.

The agent’s architecture uses complementary representations from each domain—high-resolution physical structures, population dynamics, and environmental hazards. This structure supports robust learning and inference workflows tested across diverse regions and event types.

6. Technical Innovations

Several architectural and methodological choices characterize Earth AI:

• Self-supervised and multi-task pretraining of vision transformer backbones for RS models.
• Privacy-preserving, region-level representation learning across distributed population data.
• Cross-modal alignment via jointly trained embedding spaces supporting language-driven analysis and object retrieval.
• Automated query decomposition and stepwise tool selection by the Gemini-powered agent.
• Scalable multimodal data fusion for both prediction and reasoning, ensuring robust performance at national and global scales.
• Standardized performance metrics and benchmarks for all domains, enabling direct comparison and ablation.

7. Implications for Geospatial Science and Practice

Earth AI delivers a unified framework for geospatial analysis that bridges the gap between raw data and actionable insight:

• Actionability and Decision Support: Real-time scenario analysis supports crisis management, public health forecasting, and infrastructure risk assessment.
• Interdisciplinary Synergy: The explicit combination of environmental, physical, and socioeconomic domains facilitates research in areas from sustainable urbanization to climate adaptation.
• Foundation for Future Research: The use of cross-modal, benchmark-driven evaluation sets a precedent for the development and assessment of large-scale geospatial foundation models and agentic reasoning systems in future Earth AI research.

In summary, Earth AI exemplifies a scalable, interpretable, and synergistic integration of advanced foundation models and agentic multimodal reasoning, producing a robust platform for geospatial intelligence and critical planetary inference (Bell et al., 21 Oct 2025).