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Google Earth AI

Built on years of modeling the world and Gemini’s advanced reasoning, Earth AI is helping enterprises, nonprofits and cities with everything from environmental monitoring to real-time disaster response.

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Built on years of modeling the world and Gemini’s advanced reasoning, Earth AI is helping enterprises, nonprofits and cities with everything from environmental monitoring to real-time disaster response.

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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)
Preview 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. View details
General Geospatial Inference with a Population Dynamics Foundation Model
Mohit Agarwal
Mimi Sun
Chaitanya Kamath
Arbaaz Muslim
Prithul Sarker
Joydeep Paul
Hector Yee
Marcin Sieniek
Kim Jablonski
Yael Mayer
David Fork
Sheila de Guia
Jamie McPike
Adam Boulanger
Tomer Shekel
David Schottlander
Yao Xiao
Manjit Chakravarthy Manukonda
Yun Liu
Neslihan Bulut
Sami Abu-El-Haija
Arno Eigenwillig
Bryan Perozzi
Monica Bharel
Von Nguyen
Luke Barrington
Niv Efron
Yossi Matias
Greg Corrado
Krish Eswaran
Shruthi Prabhakara
Shravya Shetty
Gautam Prasad
arXiv (2024)
Preview abstract Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations, and researchers to understand and reason over complex relationships between human behavior and local contexts. This support includes identifying populations at elevated risk and gauging where to target limited aid resources. Traditional approaches to these classes of problems often entail developing manually curated, task-specific features and models to represent human behavior and the natural and built environment, which can be challenging to adapt to new, or even related tasks. To address this, we introduce the Population Dynamics Foundation Model (PDFM), which aims to capture the relationships between diverse data modalities and is applicable to a broad range of geospatial tasks. We first construct a geo-indexed dataset for postal codes and counties across the United States, capturing rich aggregated information on human behavior from maps, busyness, and aggregated search trends, and environmental factors such as weather and air quality. We then model this data and the complex relationships between locations using a graph neural network, producing embeddings that can be adapted to a wide range of downstream tasks using relatively simple models. We evaluate the effectiveness of our approach by benchmarking it on 27 downstream tasks spanning three distinct domains: health indicators, socioeconomic factors, and environmental measurements. The approach achieves state-of-the-art performance on geospatial interpolation across all tasks, surpassing existing satellite and geotagged image based location encoders. In addition, it achieves state-of-the-art performance in extrapolation and super-resolution for 25 of the 27 tasks. We also show that the PDFM can be combined with a state-of-the-art forecasting foundation model, TimesFM, to predict unemployment and poverty, achieving performance that surpasses fully supervised forecasting. The full set of embeddings and sample code are publicly available for researchers. In conclusion, we have demonstrated a general purpose approach to geospatial modeling tasks critical to understanding population dynamics by leveraging a rich set of complementary globally available datasets that can be readily adapted to previously unseen machine learning tasks. View details
Continental-scale building detection from high resolution satellite imagery
Wojciech Sirko
Sergii Kashubin
Marvin Ritter
Abigail Annkah
Yasser Salah Eddine Bouchareb
Yann Dauphin
Daniel Keysers
Maxim Neumann
Moustapha Cisse
John Quinn
arXiv (2021)
Preview abstract Identifying the locations and footprints of buildings is vital for many practical and scientific purposes, and such information can be particularly useful in developing regions where alternative data sources may be scarce. In this work, we describe a model training pipeline for detecting buildings across the entire continent of Africa, given 50cm satellite imagery. Starting with the U-Net model, widely used in satellite image analysis, we study variations in architecture, loss functions, regularization, pre-training, self-training and post-processing that increase instance segmentation performance. Experiments were carried out using a dataset of 100k satellite images across Africa containing 1.75M manually labelled building instances, and further datasets for pre-training and self-training. We report novel methods for improving performance of building detection with this type of model, including the use of mixup (mAP +0.12) and self-training with soft KL loss (mAP +0.06). The resulting pipeline obtains good results even on a wide variety of challenging rural and urban contexts, and was used to create the Open Buildings dataset of approximately 600M Africa-wide building footprints. View details

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