Earth AI: Unlocking Geospatial Insights with Foundation Models and Cross-Modal Reasoning

TL;DR

Earth AI introduces a suite of foundation models and an intelligent reasoning engine to analyze complex geospatial data, enabling profound insights and superior predictive capabilities for understanding our planet.

Contribution

The paper presents a novel integrated framework of foundation models and a reasoning agent for geospatial analysis, addressing data complexity and multi-step query challenges.

Findings

Benchmark results show enhanced predictive accuracy.

The reasoning agent effectively handles complex, real-world scenarios.

Models provide complementary insights for geospatial inference.

Abstract

Geospatial data offers immense potential for understanding our planet. However, the sheer volume and diversity of this data along with its varied resolutions, timescales, and sparsity pose significant challenges for thorough analysis and interpretation. This paper introduces Earth AI, a family of geospatial AI models and agentic reasoning that enables significant advances in our ability to unlock novel and profound insights into our planet. This approach is built upon foundation models across three key domains--Planet-scale Imagery, Population, and Environment--and an intelligent Gemini-powered reasoning engine. We present rigorous benchmarks showcasing the power and novel capabilities of our foundation models and validate that when used together, they provide complementary value for geospatial inference and their synergies unlock superior predictive capabilities. To handle complex, multi-step queries, we developed a Gemini-powered agent that jointly reasons over our multiple foundation models along with large geospatial data sources and tools. On a new benchmark of real-world crisis scenarios, our agent demonstrates the ability to deliver critical and timely insights, effectively bridging the gap between raw geospatial data and actionable understanding.