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

Aaron Bell
Amit Aides
Amr Helmy
Arbaaz Muslim
Aviad Barzilai
Aviv Slobodkin
Bolous Jaber
David Schottlander
George Leifman
Joydeep Paul
Mimi Sun
Nadav Sherman
Natalie Williams
Per Bjornsson
Roy Lee
Ruth Alcantara
Thomas Turnbull
Tomer Shekel
Vered Silverman
Yotam Gigi
Adam Boulanger
Alex Ottenwess
Ali Ahmadalipour
Anna Carter
Behzad Vahedi
Charles Elliott
David Andre
Elad Aharoni
Gia Jung
Hassler Thurston
Jacob Bien
Jamie McPike
Jess Sapick
Juliet Rothenberg
Kartik Hegde
Kel Markert
Kim Jablonski
Luc Houriez
Monica Bharel
Ean Phing VanLee
Reuven Sayag
Shelley Cazares
Shlomi Pasternak
Stone Jiang
Thomas Colthurst
Yang Chen
Yehonathan Refael
Yochai Blau
Yuval Carny
Yael Maguire
Avinatan Hassidim
James Manyika
Tim Thelin
Genady Beryozkin
Gautam Prasad
Luke Barrington
Yossi Matias
Niv Efron
Shravya Shetty
asXiv (2025)
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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.
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