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.

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.

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.

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.

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.

Our AI weather model family, WeatherNext, helps us make predictions that are faster and more reliable than traditional methods. They're already transforming how we forecast the weather, improving areas like disaster response, grid reliability, and global food security.

NeuralGCM is a model that can rapidly, efficiently, and accurately simulate Earth’s atmosphere. It combines traditional physics-based modeling with machine learning for improved simulation accuracy and efficiency, giving scientists the ability to make accurate predictions about Earth’s climate.

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.

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.

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).

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.

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.

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.

Gain unique insights into population characteristics and their environmental interplay. Population Dynamics Foundations for Places Insights reveals the bigger picture: how humans thrive, influenced by their surroundings, and how these trends and patterns evolve over time.

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.