Google Earth AI

Built on years of modeling the world and Gemini’s advanced reasoning, Earth AI is helping enterprises, nonprofits and cities with everything from environmental monitoring to real-time disaster response.
Built on years of modeling the world and Gemini’s advanced reasoning, Earth AI is helping enterprises, nonprofits and cities with everything from environmental monitoring to real-time disaster response.
In a new technical report, we demonstrate how combining domain-specific foundation models, spatial tools, and upfront planning enables a Geospatial Reasoning agent to carry out complex spatial analysis and planetary prediction that standalone LLMs can’t. Following promising tests with partners, we’re also expanding experimental agent access via Ask Google Earth and an Agent-to-Agent (A2A) service.
Google Earth AI is our family of geospatial AI models and reasoning agents that provides users with actionable insights, grounded in real-world understanding. Today, we’re sharing our latest Earth AI innovations and expanding access to these new capabilities on Google Earth and Google Cloud.
Our population dynamics foundation model and dataset enable us to solve a wide array of geospatial problems across health, socioeconomic and environmental tasks.
Our planetary prediction engine (PPE) is 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.
With Google Earth AI’s Population Dynamics Foundation Model (PDFM), we can address the data gaps and temporal reporting lags of existing epidemiological workflows. In our latest work, we present five partner-driven case studies demonstrating how this model exemplifies the planetary geospatial foundation model paradigm for global public health.
We're helping public health leaders overcome reporting lags, forecast disease outbreaks, and deliver care to vulnerable communities around the world by combining local health data with advanced geospatial models.
We’re leveraging geospatial foundation models and generative AI to accelerate problem solving and unlock powerful insights for crisis response, public health and climate resilience.
The Methane Analysis and Plume Localization with EMIT model is a deep-learning framework that automates the detection, enhancement quantification, and source estimation of methane plumes globally, turning raw satellite data into scalable climate action.
By combining local health data with advanced geospatial models, Google Earth AI is helping the global health community predict outbreaks, identify local vulnerabilities and deliver proactive care where it is needed most.
Mobility AI uses AI advancements in measurement, simulation, and optimization to provide transportation agencies with data-driven tools for traffic management, policymaking and urban monitoring.
We’re applying S2Vec, a self-supervised framework that transforms complex geospatial data into general-purpose embeddings to predict socioeconomic and environmental patterns across the globe.
We’re expanding our global flood forecasting coverage with the roll-out of flash flood predictions in urban areas. Using a novel AI training method based on news data, we provide up to 24 hours advance notice for these rapid-onset events.
We’re advancing global research and conservation with SpeciesNet, an open-source AI tool that automatically identifies species in camera trap images.
NeuralGCM combines physics-based modeling and a neural network trained on NASA precipitation observations to simulate global precipitation more accurately than other methods, particularly for capturing the daily precipitation cycle and extreme events.
We’re using hard-braking event (HBE) data from Android Auto to identify road segments with higher crash risk, offering a leading measure for more proactive road safety assessment.
Natural Forests of the World 2020 is an AI-powered map that distinguishes natural forests from other tree cover. This critical baseline helps governments, companies, and communities meet deforestation-free goals and protect ecosystems.
We’re using high-resolution deep learning to reveal fine-scale ecological features like hedgerows and copses. This precise data provides a new pathway to address climate and biodiversity crises without compromising food security.