Google expands Earth AI: Geospatial Reasoning, Google Earth tools, and Cloud access

Google is expanding Earth AI with Geospatial Reasoning, a Gemini-powered framework that fuses multiple geospatial models to answer complex questions, and is opening a Trusted Tester program. New Earth AI imagery capabilities are coming to Google Earth, letting users find objects and patterns (like algae blooms) via natural language, with U.S. availability to Professional tiers and higher limits for AI Pro and Ultra subscribers. On Google Cloud, Earth AI models are available to Trusted Testers, and pilots with WHO AFRO, Planet, Airbus, and Alphabet’s Bellwether show practical impact from disease risk mapping to disaster and infrastructure management.

Key Points

• Geospatial Reasoning links multiple Earth AI models with Gemini to answer complex, real-world questions (e.g., vulnerability and infrastructure risk in storms), with a Trusted Tester program opening now.
• Gemini capabilities in Google Earth gain new Earth AI models so users can query imagery to detect patterns like dried rivers or algae blooms; U.S. rollout to Professional tiers in weeks, with higher limits for AI Pro and Ultra subscribers starting today.
• Earth AI models (Imagery, Population, Environment) are coming to Google Cloud for Trusted Testers, enabling businesses to combine proprietary data with Google datasets such as Imagery Insights.
• Demonstrated impact includes WHO AFRO cholera risk mapping in the DRC, Planet’s deforestation analyses, Airbus’s vegetation encroachment detection, and Bellwether’s hurricane insights for faster insurance claims.
• These advances build on Google’s geospatial AI used in floods, wildfires, cyclones, and air quality, aimed at turning rapid insights into action for environmental monitoring and crisis response.

Sentiment

Mixed. While there is clear interest and appreciation for the technical advancements and practical applications of Google Earth AI, a significant portion of the discussion expresses skepticism and cynicism regarding its commercial motivations, potential for data privatization, and the messaging around its societal benefits.

In Agreement

• The shift from static map layers to dynamic, intelligent querying ('what/where/why now?') is a significant advancement.
• Integrating Gemini into Google Earth could lower the barrier for non-GIS professionals, making complex geospatial analysis more accessible.
• LLMs like GPT 5 and Gemini are already demonstrating effectiveness in challenging geolocation tasks, validating the potential of Google Earth AI's capabilities.
• The technology has transformative potential for various sectors, such as real estate (Zillow/Redfin) and historical image analysis.
• This represents an evolutionary step from earlier geospatial analysis software (like Erdas Imagine) and its nascent machine learning capabilities.

Opposed

• Skepticism that claims about AI-powered hurricane predictions leading to faster insurance payouts are genuine, suggesting it might be a 'marketing gimmick' with underlying complexities.
• Concerns that private companies, like Google, might create expensive proprietary versions of publicly available data services (e.g., NOAA), potentially leading to a loss of public access or political interference in climate-related messaging.
• Distrust of 'corporate nonsense' associated with Google's offerings, with some preferring open-source alternatives like OpenStreetMap combined with LLM-generated queries for similar analysis.