Google Unveils Geospatial Reasoning: A Revolutionary AI Approach to Analyzing Earth Data

We're introducing a new way to analyze geospatial data.

Imagine being able to get complex insights about our planet -- like the impacts of hurricanes or best locations for city infrastructure -- simply by asking Gemini. Geospatial Reasoning, a new research effort introduced today by Google Research, aims to provide comprehensive answers about our world, complete with detailed plans and data visualizations. Google Research's novel geospatial AI foundation models, including our latest remote sensing foundation models, are trained on vast amounts of satellite and aerial imagery. These models can analyze images that are undecipherable to the human eye and support natural language queries about their features, such as changes to buildings or impassable roads. Geospatial Reasoning will combine these models with Gemini 2.5 to understand questions in natural language, and intelligently plan and orchestrate analysis across diverse geospatial data sources -- including maps, socioeconomic data, satellite imagery and your own proprietary datasets.

Google's new AI could predict disasters before they hit

Google is leveraging generative AI and multiple foundation models to introduce Geospatial Reasoning, a research initiative designed to accelerate geospatial problem-solving. This effort integrates large language models like Gemini and remote sensing foundation models to enhance data analysis across various sectors. For years, Google has compiled geospatial data, which is information tied to specific geographic locations, to enhance their products. This data is crucial for addressing enterprise challenges such as those in public health, urban development, and climate resilience. The new remote sensing foundation models are built upon architectures like masked autoencoders, SigLIP, MaMMUT, and OWL-ViT, and are trained using high-resolution satellite and aerial images with text descriptions and bounding box annotations. These models generate detailed embeddings for images and objects, and can be customized for tasks such as mapping infrastructure, assessing disaster damage, and locating specific features. These models support natural language interfaces, enabling users to perform tasks like finding images of specific structures or identifying impassable roads. Evaluations have demonstrated state-of-the-art performance across various remote sensing benchmarks. Geospatial Reasoning aims to integrate Google's advanced foundation models with user-specific models and datasets, building on the existing pilot of Gemini capabilities in Google Earth. This framework allows developers to construct custom workflows on Google Cloud Platform to manage complex geospatial queries using Gemini, which orchestrates analysis across various data sources. The demonstration application shows how a crisis manager can use Geospatial Reasoning after a hurricane by: The demonstration application includes: The application uses high-resolution aerial images from the Civil Air Patrol, pre-processed with AI from Bellwether, X's moonshot for Climate Adaptation, plus Google Research's Open Buildings and SKAI models. Social vulnerability indices, housing price data, and Google WeatherNext insights are also incorporated. WPP's Choreograph will integrate PDFM with its media performance data to enhance AI-driven audience intelligence. Airbus, Maxar, and Planet Labs will be the initial testers of the remote sensing foundation models.