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GOOGLE EARTH AI
Transform planetary information into actionable intelligence
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 disaster response.
Breakthroughs in understanding the Earth that previously required complex analytics and years of iteration are now made possible in a matter of minutes.
You Ask, The Earth Answers
Earth AI models power features used by billions, and provide actionable insights in Google Earth, Google Maps Platform, and Google Cloud.
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Where are all the storm drains within 50 meters of public schools in Red Hook, Brooklyn?
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How can AI help to better predict the path and intensity of cyclones?
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Which communities within the Democratic Republic of Congo are most at risk for cholera outbreaks?
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Which global mangrove forests are most similar to mangroves in the Mekong delta in Vietnam?
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The global partners forging the future with Earth AI
Our partners have been using Earth AI models to help with critical business decision making for multiple years. These featured partners are using the latest Earth AI models and datasets.
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Transforming planetary data into actionable intelligence with Google Earth AI
See how Google Earth AI unites geospatial models with Gemini-powered reasoning. Learn how partners like Planet, Airbus, Deloitte, Boston Children's Hospital, and GiveDirectly use Earth AI to move from manual image analysis to instant, high-value insights. The platform accelerates analysis, enhances disaster preparedness, and drives better public health and business decisions worldwide.
From satellite imagery to queryable insights with Planet Labs and Airbus
Leading satellite providers like Planet Labs, and Airbus leverage Google's Remote Sensing Foundation Models (RSFM) as part of a trusted tester program. This collaboration accelerates their AI development, enabling instantaneous object detection and image search using natural language. Discover how Imagery models unlock unparalleled planetary monitoring, massive scale change detection, and accelerated map-making capabilities.
Solving global population challenges with geospatial AI with WPP, Deloitte and Boston Children's Hospital
Discover how Google Earth AI and its foundation models power partners like WPP, Deloitte, and Boston Children's Hospital. Learn how they use Earth AI to tackle multi-dimensional problems, from marketing and urban planning to predicting public health risks and targeting life-saving interventions. Google Earth AI gives decision-makers actionable insights at an unprecedented scale and speed.
Geospatial AI for governments and crisis response with McGill and Partners and GiveDirectly
See how Google Earth AI enables a faster, more effective response. Bellwether, an X moonshot, is using Earth AI to provide insurance broker McGill and Partners predictive analysis of property damage before a storm strikes, which helps their clients pay claims faster so homeowners can start rebuilding sooner — saving them time, money and stress. Humanitarian partner GiveDirectly uses the Google Flood Hub for anticipatory cash assistance before floods hit.
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For years, we’ve been building models about the world, including floods, wildfires, air quality, and cyclones, providing trusted information to billions of people and organizations. For example, our flood forecasting — information now covering more than two billion people — provides life-saving forecasts before significant river floods.
Explore geospatial analytics on Google Cloud's Gemini Enterprise Agent Platform
To deliver actionable, location-aware intelligence, Gemini Enterprise Agent Platform is designed for native grounding using Google Maps' unique, high-fidelity data. This capability instantly contextualizes your AI applications and agents with information on over 300 million places, providing the local and geospatial insights necessary for accurate modeling, resilient operations, and informed decision-making in the physical world.
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Imagery
Unifying remote sensing data with cutting-edge AI for diverse geospatial analysis and forecasting, from land use to object detection.
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Search the earth using natural language with Imagery models on Gemini Enterprise Agent Platform
Specialized AI models enable finding objects, classifying scenes or monitoring changes across Earth's observation imagery. Query the Earth with natural language and significantly accelerate remote sensing workflows, such as identifying where harmful algae is blooming in order to monitor drinking water supply, giving authorities time to issue warnings or shut down water utilities.
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AlphaEarth Foundations helps map our planet in unprecedented detail
AlphaEarth Foundations integrates petabytes of Earth observation data to generate a unified representation, empowering users to perform faster, more accurate geospatial analyses like change detection, map segmentation and classification.
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Explore the data in Earth Engine
Searching satellite imagery with AI
Gemini capabilities in Google Earth now integrate new Earth AI models, allowing users to instantly find objects and discover patterns across satellite imagery. This experimental feature streamlines environmental monitoring—such as spotting dried rivers or algae blooms—for Professional and Professional Advanced users in the US.
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Put the globe to work with Google Earth
Create, analyze, and collaborate with Google's unique imagery and global knowledge, cloud infrastructure, and community-scale generative building and solar designs. Make faster, informed decisions in early-stage projects—no GIS training required.
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Elevating Vantor’s AI-powered analytics
Vantor is integrating Google Earth AI imagery models into its Tensorglobe platform to help government and commercial organizations respond to a rapidly changing world with greater clarity and confidence. By combining Earth AI imagery foundation models with Vantor’s AI-ready spatial foundation—built on a 20+ year satellite imagery archive and their advanced global 2D and 3D data—organizations can fuse vast volumes of sovereign and third-party imagery into timely, actionable insight.
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Forecasting deforestation risk
ForestCast, the first deep learning benchmark for proactive deforestation risk forecasting, is a model that utilizes pure satellite data to predict future forest loss accurately and at scale, overcoming the limitations of older methods that relied on inconsistent, region-specific input maps. This marks a fundamental shift from monitoring past losses to actively predicting and preventing future environmental threats.
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Mapping natural forests with AI
Natural Forests of the World 2020, an AI-powered baseline map for deforestation and degradation monitoring. This critical resource achieves best-in-class accuracy at 10-meter resolution in distinguishing natural forests from other tree cover, aiding companies (e.g., for EUDR compliance), governments, and conservation groups worldwide.
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Open Buildings uses AI to put everyone on the map
This open dataset maps 1.8 billion buildings in the Global South and includes changes in development from the years 2016-2023. It uses AI to super-resolve and extract building footprints and heights from Sentinel-2 satellite imagery. For example, it's allowed us to greatly expand coverage of our Solar API.
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Advanced Geospatial AI using Google Imagery
Together, Aerial & Satellite Insights and Street View Insights put global, high-resolution imagery at your fingertips. It expands the capability from the fine details of a city street to a bird’s-eye view of the planet, empowering you to make data-driven decisions at any scale. In addition, Aerial & Satellite Models are designed to transform your geospatial workflows by enabling you to extract planetary-scale insights without the need to train or maintain complex models yourself.
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Environment
Harnessing AI to deliver actionable, high-resolution weather and climate insights, from real-time forecasts to disaster response and policy impact.
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Using AI to make critical flood forecasting information universally accessible
Our AI flood forecast provides reliable riverine flood warnings up to seven days. Flood Hub currently covers river basins in over 100 countries worldwide, providing critical flood forecasting to a population of 700M people.
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Forecasting global weather with AI
WeatherNext is an AI-powered ensemble forecasting model for global weather prediction. It utilizes a novel Functional Generative Network architecture, which enables it to generate forecasts 8x faster and with resolution up to 1-hour. This state-of-the-art model delivers more accurate global weather predictions - including for extreme weather events - aiding enterprises, governments, and researchers worldwide.
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Try now in Big Query and Earth Engine
Better tropical cyclone predictions with AI
Weather Lab features our latest experimental AI-based tropical cyclone model, based on stochastic neural networks. This model can predict a cyclone’s formation, track, intensity, size and shape — generating 50 possible scenarios, up to 15 days ahead.
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Accelerating wildlife image classification
Camera traps generate vast amounts of data, but manually identifying millions of wildlife photos can take decades and creates a major bottleneck for conservation research. One year after its open-source release, the SpeciesNet model is empowering researchers to classify nearly 2,500 animal species—processing massive datasets from the savannas of Tanzania to the forests of Idaho—streamlining global biodiversity monitoring and helping conservationists quickly translate raw imagery into actionable insights to protect threatened populations.
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Predicting urban flash flood events
For years, a lack of high-fidelity historical data prevented AI models from predicting flash floods before they happen. Groundsource is a new AI-powered methodology that transforms public information into a high-quality record of historical disaster data – starting with flash floods in urban areas. Groundsource uses Gemini to analyze decades of public reports and identifies over 2.6 million historical flood events spanning more than 150 countries. It then used Google Maps to determine precise geographic boundaries for each event to create a dataset focused on flash floods. Using this dataset we trained a new model now available on Google’s Flood Hub to predict flash floods in urban areas up to 24 hours in advance, to help ensure no one is surprised by a natural disaster.
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Transforming conservation efforts
Google’s novel tools aimed at helping us better understand and protect Earth's biosphere include a benchmark dataset for high-resolution deforestation risk prediction, a new approach for species range mapping at unprecedented scale, and Perch 2.0, a state-of-the-art bioacoustics model for automated ecosystem health monitoring and identifying endangered species.
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NeuralGCM for improved atmospheric simulations
NeuralGCM is a hybrid atmospheric model combining traditional physics with machine learning for faster, accurate weather simulations. By using physical laws for large-scale dynamics and ML for small-scale phenomena, it produces high-quality forecasts at a fraction of the cost. This open-source model significantly increases accessibility for researchers.
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NeuralGCM in action
Air quality data and insights to help people reduce their exposure to pollution
Powering air quality information for millions of users on Google Search and Maps, our advanced Air Quality API uses AI to fuse satellite, weather, and traffic data, delivering highly accurate, real-time Air Quality Index (AQI) forecasts at a 500-meter global resolution.
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Limit the risks of exposure to allergenic pollen
Our Pollen API can be used to help people limit the risks of exposure to allergenic pollen and make better informed daily decisions. The pollen model combines AI with physical and biological modeling to generate a 5-day high-resolution forecast across over 65 countries. The model is trained to recognize the exact location and density of specific tree, grass and weed species to predict the Universal Pollen Index (UPI).
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Provide accurate wildfires information to affected communities and fire authorities
We use satellite imagery and ML to detect and track wildfires, making information available via Google Search and Maps, informing affected communities and helping fire authorities take action, and developing a simulator to generate data in a range of wildfire scenarios.
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Population
Unifying search trends, demographics, business, mobility and geospatial data with AI to understand the complex interplay of populations and places.
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Assessing MMR vaccination coverage gaps
Measles outbreaks are rising, but routine vaccination data are often delayed and too coarse to pinpoint local gaps. Researchers at Harvard, Mount Sinai, and Boston Children’s Hospital combined survey data with Google’s Population Dynamics Foundation Model (PDFM) to produce “superresolution” estimates of MMR coverage among young children—at much finer geographic scales than standard reporting, down to the ZIP-code level—revealing clusters of undervaccination that align with recent outbreaks and helping public health teams prioritize locally targeted outreach and prevention efforts.
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De-risking city investments with Traffic Simulation API
Traffic Simulation API leverages AI advancements in measurement, simulation, and optimization to provide transportation agencies with powerful tools for baseline simulation of city networks, allowing them to modify and test the network-wide impact of proposed infrastructure changes, as well as temporary disruptions such as lane or road closures due to maintenance, mass events, or crashes. It uses high-fidelity modeling to provide granular results on vehicle counts and average speeds, giving users detailed data on the cascading effects across the entire road network to de-risk investments.
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Insights on the dynamics of human behavior across the planet
Population Dynamics Foundations distills data about human behavior and our interactions with the environment into concise, analysis-ready embeddings. Now expanded to include more countries and capture changes over time, Population Dynamics Foundations insights are enhancing applications including disease modeling, mapping vulnerable communities and location-based marketing.
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Advancing urban transportation with Mobility AI
Mobility AI leverages AI advancements in measurement, simulation, and optimization to provide transportation agencies with powerful tools for data-driven policy making, traffic management, and continuous monitoring of urban transportation systems. Powering Roads Management Insights on Google Maps Platform.
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Build safer, smarter road networks with Roads Management Insights
By integrating real-time disruption data with operational and volume metrics, organizations can gain a comprehensive view of road conditions and safety. Real-time disruption feeds provide immediate alerts on road closures, incidents, and environmental hazards. Complementing these safety signals, vehicle counts offer deep analytics on traffic volume, allowing for more precise maintenance scheduling and sophisticated pattern analysis.
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Train better predictive models with geospatial embeddings
Population Dynamics Insights is a first-of-its-kind geospatial embeddings dataset designed to help organizations decode the complex relationship between human behavior and the physical world. By distilling anonymized trends from Google Search and Google Maps into rich 330-dimensional vectors for locations across the globe, it enables a new era of spatial machine learning without the need for manual feature engineering.
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Introducing Geospatial Reasoning
Geospatial Reasoning is making it possible to tackle multimodal Earth AI challenges. Gemini-powered agents enable developers, data analysts, and scientists to integrate Google’s advanced Earth AI models with their own models and datasets. Now you can make natural language queries about the physical world and get deep, actionable insights, grounded in real-world understanding.
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Powering features that billions rely on across Google
When major climate events strike, Google products like Search and Maps help billions of people make critical decisions to stay safe.
Nowcasting brings AI-powered weather forecasts to Google Search
MetNet, an AI nowcasting model, predicts precipitation with high accuracy via satellite data. This fills gaps in radar coverage, providing better rain predictions on Google Search.
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Enabling a more immersive, intuitive Google Maps experience
With Immersive View, you’re able to experience what a neighborhood, landmark, restaurant or popular venue is like — and even feel like you’re right there before you ever leave the house. So whether you’re traveling somewhere new or scoping out hidden local gems, Immersive View helps you make the most informed decisions before you go.
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Informing communities with early storm forecasts and alerts
People turn to Google Search and Maps in times of crises, to find early warnings of extreme weather events. Crisis notification cards appear on Google Maps for those near the impacted area, directing to a hurricane forecast of the storm’s trajectory along with information about what time it’s likely to hit certain areas.
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In 2024, four of our geospatial products—Google Earth, Solar API, fuel-efficient routing in Google Maps, and Green Light—enabled individuals, cities, and other partners to collectively reduce an estimated 18 million metric tons of GHG emissions (tCO2e), roughly equivalent to the emissions from the annual energy use of over 2.4 million U.S. homes.¹
¹To estimate aggregate enabled emissions reductions, we first estimated annual reductions for the products individually (Google Earth Pro, Solar API, fuel-efficient routing, and Green Light) and then combined the totals. For details about the individual calculation methodologies, refer to Google’s 2025 Environmental Report. For equivalencies, we used the "Greenhouse Gas Equivalencies Calculator,” U.S. Environmental Protection Agency, November 2024, accessed October 2025.
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