When you get the best route from Google Maps, explore a new place in Street View, look at your neighbourhood on Google Earth, or check the weather forecast with Search, you’re using geospatial data. For decades, Google has organized the world’s geospatial information — data associated with a specific geographical location — and made it accessible through our products.

Geospatial information is essential in everyday situations and for a wide range of real-world enterprise problems. Whether you’re working in public health, urban development, integrated business planning, or climate resilience, Google’s data, real-time services, and AI models can accelerate your analyses and augment your proprietary models and data.

Geospatial information can be big, complex and hard to understand — just like the real world! Gathering, storing and serving data requires specialized sensors and platforms. Observations of the things you care about can be scarce or require time-consuming labelling. Use-cases are diverse and often require various kinds of data that need to be aligned and cross-referenced (weather, maps, images, etc.), and recent breakthrough AI methods are not optimized for geospatial problems. Transforming geospatial information into understanding is a focus area for Google Research.

Last November we introduced two pre-trained, multi-purpose models to address many of the challenges of geospatial modeling: the Population Dynamics Foundation Model (PDFM), which captures the complex interplay between population behaviors and their local environment, and a new trajectory-based mobility foundation model. Since then, over two hundred organizations have tested the PDFM embeddings for the United States and we are expanding the dataset to cover the UK, Australia, Japan, Canada, and Malawi for experimental use by selected partners.

We’re also exploring how generative AI can reduce the significant cost, time, and domain expertise required to combine geospatial capabilities. Large language models (LLMs) like Gemini can manage complex data and interact with users through natural language. When integrated into agentic workflows that are grounded in geospatial data, we’re starting to see that they can generate insights in various domains that are both surprising and useful.

Today, we’re introducing new remote sensing foundation models for experimentation alongside a research effort called Geospatial Reasoning that aims to bring together all of our foundation models with generative AI to accelerate geospatial problem solving. Our models will be available through a trusted tester program, with inaugural participants including WPP, Airbus, Maxar, and Planet Labs.