Source Google Research · Published · Open the original ↗
Lab notes
Putting Earth AI to work: Geospatial agents for our planet's biggest challenges
Earth AI
In a new technical report, we demonstrate how combining domain-specific foundation models, spatial tools, and upfront planning enables a Geospatial Reasoning agent to carry out complex spatial analysis and planetary prediction that standalone LLMs can’t. Following promising tests with partners, we’re also expanding experimental agent access via Ask Google Earth and an Agent-to-Agent (A2A) service.
When we introduced Google Earth AI , we shared a suite of geospatial foundation models, spanning remote sensing, weather, flood hydrology, and population dynamics, alongside an initial prototype showing how a Gemini-powered agent could connect those models to answer cross-modal questions.
Since then, we have evolved a Geospatial Reasoning agent from an early concept into a much more capable, experimental system built for multi-step, geospatial reasoning across planet-scale data and autonomous training of custom predictive models. Today, we are sharing three updates:
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