Shelley Cazares

Applied research scientist serving as a Machine Learning Technical Consultant in Google Public Sector (Cloud)
Authored Publications
Sort By
  • Title
  • Title, descending
  • Year
  • Year, descending
    Earth AI: Unlocking Geospatial Insights with Foundation Models and Cross-Modal Reasoning
    Aaron Bell
    Aviad Barzilai
    Roy Lee
    Gia Jung
    Charles Elliott
    Adam Boulanger
    Amr Helmy
    Jacob Bien
    Ruth Alcantara
    Nadav Sherman
    Hassler Thurston
    Yotam Gigi
    Bolous Jaber
    Vered Silverman
    Luke Barrington
    Tim Thelin
    Elad Aharoni
    Kartik Hegde
    Yuval Carny
    Shravya Shetty
    Yehonathan Refael
    Stone Jiang
    David Schottlander
    Juliet Rothenberg
    Luc Houriez
    Yochai Blau
    Joydeep Paul
    Yang Chen
    Yael Maguire
    Aviv Slobodkin
    Shlomi Pasternak
    Alex Ottenwess
    Jamie McPike
    Per Bjornsson
    Natalie Williams
    Reuven Sayag
    Thomas Turnbull
    Ali Ahmadalipour
    David Andre
    Amit Aides
    Ean Phing VanLee
    Niv Efron
    Monica Bharel
    arXiv (preprint 2025), arXiv, arXiv:2510.18318 https://doi.org/10.48550/arXiv.2510.18318 (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. The emergence of Foundation Models (FMs) and Large Language Models (LLMs) offers an unprecedented opportunity to tackle some of this complexity, unlocking novel and profound insights into our planet. This paper introduces a comprehensive approach to developing Earth AI solutions, 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 they provide complementary value to improve geospatial inference. We show that the synergy between these models unlocks 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 to unlock novel geospatial insights. 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