AI Foundation Models for Earth, Space and Planetary Sciences

Alemohammad, Hamed

IN010
AI Foundation Models for Earth, Space and Planetary Sciences

Session ID#: 252587

Session Description:

Foundation models (FMs) are transforming scientific discovery across disciplines. These generalized AI models are trained through self-supervised methods for a plethora of downstream applications and eliminate the need for extensive labeled datasets. This session will explore their growing role in Earth, Space, and Planetary sciences. Topics of interest include but are not limited to:1) Pretraining strategies and training data curation2) Intermediate learning strategies to enhance FMs’ domain adaptability3) Development of multimodal, multiscale, and spatiotemporal-aware FMs4) Emerging strategies for for fine-tuning FMs5) Cross-domain FMs that bridge Earth, Space, and Planetary sciences6) Interpretability, uncertainty quantification, and trustworthiness of FM outputs7) Key science applications of FMsThe session also encourages discussion on broad community involvement toward development of open FMs for science that are accessible for all. Our goal is to foster discussion about how FMs are shaping the next generation of scientific tools and workflows.

Co-Sponsor(s):

B - Biogeosciences
H - Hydrology
P - Planetary Sciences
SH - SPA-Solar and Heliospheric Physics

Index Terms:

1906 Computational models, algorithms [INFORMATICS]
1920 Emerging informatics technologies [INFORMATICS]
1942 Machine learning [INFORMATICS]

Primary Convener: Hamed Alemohammad, PhD, Clark University, Clark Center for Geospatial Analytics, Worcester, United States

Conveners: Wenwen Li, Arizona State Univ, School of Geographical Science and Urban Planning, Tempe, United States and Ankur Kumar, University of Alabama in Huntsville, Earth System Science Center, Huntsville, United States

Student/Early Career Convener: Rufai Balogun, Clark University, Clark Center for Geospatial Analytics, Worcester, United States

Accelerating Earth System Simulations: Creating an AI-Powered Emulator for the E3SM Land Model (1988603)

Dali Wang¹, Peter E Thornton², Daniel M Ricciuto², Xiaojuan Yang², Xiao Wang³, Dan Lu⁴, Dawei Gao⁵, Yunhe Feng⁶, Qinglei Cao⁷, Zhuowei Gu⁷ and Zirui Liu⁸, (1)Oak Ridge National Laboratory, Climate Change Science Institute and Environmental Sciences Division, Oak Ridge, TN, United States, (2)Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, United States, (3)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (4)Oak Ridge National Laboratory, Oak Ridge, United States, (5)University of North Texas, Denton, United States, (6)AGH University of Science and Technology, Kraków, Poland, (7)Saint Louis University Main Campus, Saint Louis, United States, (8)University of Minnesota, Minneapolis, United States

Active Region Emergence Prediction Using the HelioFM Model (1909269)

Spiridon Kasapis, Princeton University, Department of Astrophysical Sciences, Princeton, United States, Andrés Muñoz-Jaramillo, Southwest Research Institute Boulder, Boulder, United States, Rohit Lal, University of Alabama in Huntsville, Huntsville, United States, Daniel da Silva, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Irina Kitiashvili, NASA Ames Research Center, Moffett Field, CA, United States, Alexander G Kosovichev, New Jersey Institute of Technology, Physics, Newark, United States, Sujit Roy, NASA Marshall Space Flight Center, Huntsville, United States and The NASA-IBM AI4Science Collaboration

AI Foundation Models for Observational and Gridded Data Across Scales (1968987)

Johannes Schmude¹, Johannes Jakubik², Daniel Salles Civitarese³, Shraddha Singh⁴, Sujit Roy⁵, Manil Maskey⁶, Campbell Watson⁷, Rahul Ramachandran⁵, Juan Bernabe-Moreno⁸ and Björn Lütjens¹, (1)IBM Research, Yorktown Heights, United States, (2)IBM Research, Zurich, Switzerland, (3)IBM Research, Rio de Janeiro, Brazil, (4)IBM, Sustainability Software, Austin, United States, (5)NASA Marshall Space Flight Center, Huntsville, United States, (6)NASA, Huntsville, United States, (7)IBM Research USA, Yorktown Heights, United States, (8)IBM Research, Mulhuddart, Ireland

Automated Detection of Intermittent Streams Using SAM2 and High-Resolution Satellite Imagery (2000003)

John Vicino, Universities Space Research Association Moffett Field, Mountain View, United States, Ata Akbari Asanjan, Universities Space Research Association Moffett Field, Greenbelt, United States, Peter Claggett, United States Geological Survey, Annapolis, United States and David Bell, Universities Space Research Association Moffett Field, Moffett Field, United States

Earth Index Toolkit: Human‑in‑the‑Loop Classification with Foundation‑Model Embeddings (1963743)

Alice Durieux¹, Benjamin Strong¹, Edward Boyda¹, Hutch Ingold¹, Mikel Maron¹ and Christopher Ren², (1)The Earth Genome, Los Altos, United States, (2)Demeter Labs, Santa Fe, United States

Exploring Prithvi WXC Adaptation from MERRA2 to ERA5 (1995775)

Jorge Luis Luis Guevara Guevara Diaz¹, Victor Nascimento Ribeiro², Anne Jones³, Chris Lucas⁴, Jorge Sebastián Moraga⁴, Natalie Lord⁵, Andrew Taylor⁶, Will Trojak⁷ and Edward Lockhart⁸, (1)IBM Research, Rio de Janeiro, Brazil, (2)IBM Research, IBM Research, Sao Paulo, Brazil, (3)IBM Research Europe, Daresbury, United Kingdom, (4)Fathom, Bristol, United Kingdom, (5)Fathom, Fathom, UK, United Kingdom, (6)Science and Technology Facilities Council, Daresbury, United Kingdom, (7)IBM Research, Daresbury, United Kingdom, (8)STFC Researcher, London, United Kingdom

Extending INDUS Models for Low-Latency, High-Context Scientific Sentence Embeddings (1957403)

Vishnudev Kuruvanthodi¹, Bishwaranjan Bhattacharjee², Mohab Elkaref³, Geeth De Mel⁴, Nishan Pantha⁵, Sajil Awale⁵ and Muthukumaran Ramasubra⁵, (1)IBM Research Europe, Hursley, United Kingdom, (2)IBM Research USA, Yorktown Heights, United States, (3)IBM Research Europe, Warrington, United Kingdom, (4)IBM Research UK, Hampshire, United Kingdom, (5)University of Alabama in Huntsville, Huntsville, United States

Fine-Tuning Prithvi-EO-2.0 foundation Model for High-Resolution Irrigation Mapping with Limited Labeled Data (1993439)

Ehsan Jalilvand, PhD^1,2, Sujay V Kumar³, Hamed Alemohammad, PhD⁴, Sam Khallaghi⁴, Neha Bhalla⁵, Naren Nair⁶ and Simone Vinay⁷, (1)NASA Goddard Space Flight Center, Hydrological Sciences Laboratory, Greenbelt, United States, (2)Science Applications International Corporation (SAIC), Reston, United States, (3)NASA Goddard Space Flight Center, Greenbelt, United States, (4)Clark University, Center for Geospatial Analytics, Worcester, United States, (5)Emerson High School, McKinney, United States, (6)Glenelg High School, Glenelg, United States, (7)Leland High School, San Jose, United States

From Data to Discovery: Visualizing Inputs, Reconstructions, and Artifacts in Spatiotemporal Foundation Models with SatVision‑Pix4D (1986256)

Seneca Oehrle¹, Jordan Caraballo-Vega², Jie Gong³, Stephen Zhang⁴, Sidh Jaddu⁵, Mark Carroll², Jian Li⁶, Gregory Elsaesser⁷ and Leah Ding⁸, (1)Sidwell Friends School, Hyattsville, MD, UNITED STATES, (2)NASA Goddard Space Flight Center, Data Science Group, Greenbelt, United States, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (4)Montgomery Blair High School, Potomac, United States, (5)Thomas Jefferson High School for Science and Technology, Fairfax, VA, UNITED STATES, (6)NASA Goddard Space Flight Center, Data Science Group, Greenbelt, MD, United States, (7)Columbia University/NASA GISS, New York, United States, (8)American University, Department of Computer Science, Washington, DC, United States

From Gaps to Storms: Leveraging GAIA Representations for Global Precipitation Estimation and Extreme Weather Segmentation (1999318)

Olivia Morgan Alexander¹, Jad Makki², Ata Akbari Asanjan³, Tom Berg⁴, Matt Yang², Cara Crawford⁵, Disha Shidham⁶, Clara Zhang², Srija Chakraborty⁷, Stephen Peng², Arun Ravindran², Olivier Raiman⁸, David Potere² and David Bell¹, (1)Universities Space Research Association Moffett Field, Moffett Field, United States, (2)Boston Consulting Group (BCG) X, Boston, United States, (3)Universities Space Research Association Moffett Field, Greenbelt, United States, (4)Boston Consulting Group (BCG) X, Los Angeles, United States, (5)Boston Consulting Group (BCG) X, Washington DC, United States, (6)Universities Space Research Association Moffett Field, Moffett Field, CA, United States, (7)Universities Space Research Association, Earth from Space Institute, Washigton DC, United States, (8)Universities Space Research Association Moffett Field, Paris, France

From Orbits to Insights: A Foundation Model for Lunar Science (Invited) (1930954)

Andrew Annex¹, Michael K. Barker², Zachary Morse^3,4, Vishnu Viswanathan^2,5, Sujit Roy⁶, Daniela Szwarcman⁷, Vishal Gaur⁸, Ankur Kumar⁸, Himanshu Patil⁸, Gabrielle Nyirjesy⁹, Rachel Slank¹⁰, Paolo Fraccaro¹¹, Campbell Watson¹², Juan Bernabe-Moreno¹³ and Rahul Ramachandran⁶, (1)NASA Ames Research Center, Planetary Systems Branch (STT), Moffett Field, United States, (2)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (3)NASA Goddard Space Flight Center, Greenbelt, United States, (4)Howard University, Washington, United States, (5)University of Maryland Baltimore County, Baltimore, MD, United States, (6)NASA Marshall Space Flight Center, Huntsville, United States, (7)IBM Research, Rio de Janeiro, Brazil, (8)University of Alabama in Huntsville, Huntsville, United States, (9)IBM Research USA, Yorktown Heights, NY, United States, (10)Universities Space Research Association Huntsville, Huntsville, United States, (11)IBM Research, Daresbury, United Kingdom, (12)IBM Research, Yorktown Heights, United States, (13)IBM Research, Mulhuddart, Ireland

From Pixels to Patterns: TerraMind at Meter Resolution with Weather Context (Invited) (1974699)

Johannes Jakubik¹, Benedikt Blumenstiel¹, Thomas Brunschwiler¹, Paolo Fraccaro², Campbell Watson³, Juan Bernabe-Moreno⁴ and Nicolas Longepe⁵, (1)IBM Research, Zurich, Switzerland, (2)IBM Research, Daresbury, United Kingdom, (3)IBM Research USA, Yorktown Heights, United States, (4)IBM Research, Mulhuddart, Ireland, (5)ESA, Phi-Lab Explore Office, Frascati, Italy

Guard-railing Geospatial Foundation Models (1992659)

Reginald Bryant¹, Rosie Lickorish², Anne Jones³, Paolo Fraccaro⁴, Fred Otieno⁵, Charles Wachira¹ and Blair Edwards⁶, (1)IBM Research Africa, Nairobi, Kenya, (2)IBM Research, Hursley, United Kingdom, (3)IBM Research Europe, Daresbury, United Kingdom, (4)IBM Research, Daresbury, United Kingdom, (5)Organization Not Listed, Washington, DC, United States, (6)IBM Research, Europe, Daresbury, United Kingdom

HydroGem: A Domain-Informed Foundation Model for Hydrological Anomaly Detection, Imputation, and Forecasting (1930254)

Ijaz Ul Haq¹, Byung Suk Lee¹, Donna M Rizzo², Kristen Underwood³ and Julia N Perdrial⁴, (1)University of Vermont, Computer Science, Burlington, VT, United States, (2)University of Vermont, Department of Civil and Environmental Engineering, Burlington, United States, (3)University of Vermont, Civil & Environmental Engineering; Water Resources Institute, Burlington, United States, (4)University of Vermont, Department of Geography & Geoscience, Burlington, VT, United States

Insights from Comparing Prithvi‑EO Transformer Embeddings and NASA Harmonized Landsat‑Sentinel (HLS) Spectral Profiles for Land‑Use Classification (1927935)

K M Ashraful Islam and Robert E Kennedy, Oregon State University, Corvallis, OR, United States

Integrating Prototype-based Channel-specific Reasoning in Explainable Neural Networks for Geospatial Learning Tasks (1949883)

Anushka Narayanan, Brown University, Data Science Institute and Department of Earth, Environmental and Planetary Sciences, Providence, RI, United States and Karianne J. Bergen, Brown University, Data Science Institute and Department of Earth, Environmental and Planetary Sciences, Providence, United States

Investigating the Benefits of the Foundation Model for Mars Science Applications (1969421)

Mirali Purohit¹, Bhanu Tokas², Steven Lu³, Jacob B Adler², Scott Dickenshied⁴, Serina Diniega³, Umaa Rebbapragada⁵ and Hannah Rae Kerner⁶, (1)Arizona State University, Tempe, AZ, United States, (2)Arizona State University, School of Computing and Augmented Intelligence, Tempe, United States, (3)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, (4)Arizona State University, School of Earth & Space Exploration, Mars Space Flight Facility, Tempe, United States, (5)Jet Propulsion Laboratory, California Institute of Technology, Machine Learning and Instrument Autonomy, Pasadena, United States, (6)Arizona State University, School of Computing and Augmented Intelligence, Tempe, AZ, United States

Leveraging AI multimodal geospatial foundation models for improved flood extent mapping (1910036)

Mirela G Tulbure¹, Júlio Caineta², Mark Broich³, Hamed Alemohammad, PhD⁴, Philippe Rufin Dr.⁵, Mollie Gaines², Leon Thomas⁶, Patrick Hostert⁷ and Jan Hemmerling⁸, (1)North Carolina State University Raleigh, Center for Geospatial Analytics, Raleigh, NC, United States, (2)North Carolina State University, Forestry and Environmental Resources, Raleigh, United States, (3)University of New South Wales, Sydney, NSW, Australia, (4)Massachusetts Institute of Technology, Cambridge, United States, (5)Humboldt University of Berlin, Geography, Berlin, Germany, (6)Department of Agricultural Sciences, University of Helsinki, Helsinki, Finland, (7)Humboldt-Universität zu Berlin, Geography Department, Berlin, Germany, (8)Thünen Institute of Farm Economics, Thünen Earth Observation, Thünen, Germany

Leveraging Geospatial Foundation Models for Guided Super-Resolution in Earth Observation (1982532)

Hayato Ushijima Mwesigwa, Fujitsu Research of America, San Jose, United States, Mirali Purohit, Arizona State University, Tempe, AZ, United States, Zhining Gu, Arizona State University, Tempe, United States and Ankit Kulshrestha, Fujitsu Research of America, Santa Clara, United States

Neural Emulator for Forecasting PFSS Coronal Field Extrapolation with HelioFM (1925935)

Daniel da Silva, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Andrés Muñoz-Jaramillo, Southwest Research Institute Boulder, Boulder, CO, United States, Vishal Gaur, University of Alabama in Huntsville, Huntsville, United States and Sujit Roy, University of Alabama in Huntsville, Huntsville, AL, United States

OceanRep: A Foundation Model for Ocean Dynamics (1872095)

Kacper Nowak, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, GERMANY, Christian Lessig, Otto-von-Guericke Universität, Magdeburg, Germany, Thomas Jung, AWI, Bremerhaven, Germany, Nikolay Koldunov, Alfred Wegener Institute Helmholtz-Center for Polar and Marine Research Bremerhaven, Climate Dynamics, Bremerhaven, Germany, Sergey Danilov, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany and Ilaria Luise, CERN, Geneve, Switzerland

Opportunities and challenges in moderate resolution annual change detection with Prithvi 2.0 (1884718)

Myscon Truong^1,2, Robert E Kennedy³, Rahul Ramachandran⁴, Sujit Roy⁴ and Al Kirschbaum⁵, (1)Spatial Informatics Group, LLC, Alameda, CA, United States, (2)Oregon State University, Corvallis, United States, (3)Oregon State University, Corvallis, OR, United States, (4)NASA Marshall Space Flight Center, Huntsville, United States, (5)National Parks Service, Ashland, United States

Performance and Generalizability Impacts of Fusing Location Encoder Foundation Models for Dynamic PM_2.5 Estimation (2001441)

Zhongying Wang¹, Morteza Karimzadeh¹ and James Crooks², (1)University of Colorado Boulder, Department of Geography, Boulder, United States, (2)National Jewish Health, Denver, CO, United States

Pretrain Here: How the Spatial Distribution of Pretraining Data Shapes Remote Sensing Transferability (1900219)

Amandeep Kaur, Arizona State University, Tempe, United States, Esther Rolf, University of Colorado at Boulder, Boulder, United States and Hannah Rae Kerner, Arizona State University, School of Computing and Augmented Intelligence, Tempe, AZ, United States

Prompt2DEM: High-Resolution DEMs for Urban and Open Environments from Global Prompts Using a Monocular Foundation Model (1883124)

Osher Rafaeli, Tal Svoray and Ariel Nahlieli, Ben-Gurion University of the Negev, Beer Sheva, Israel

Quantitative Evaluation of Foundation Models for Weather Forecasting (1874846)

Katherine Breen¹, Jordan Caraballo-Vega², Jian Li³, Glenn Tamkin⁴, Arlindo daSilva⁵, Donifan Barahona⁶, Kara Lamb⁷, Nathan Arnold⁸, Michael Hendrickson⁹, Min-Jeong Kim¹⁰, Andrea Molod⁵, Melanie J Frost¹¹, William Putman¹² and Mark Carroll², (1)NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, United States, (2)NASA Goddard Space Flight Center, Data Science Group, Greenbelt, United States, (3)NASA Goddard Space Flight Center, Data Science Group, Greenbelt, MD, United States, (4)NASA Center for Climate Simulation, Greenbelt, MD, United States, (5)NASA Goddard Space Flight Center, Greenbelt, United States, (6)NASA GSFC, Greenbelt, United States, (7)Columbia University, Earth and Environmental Engineering, New York, United States, (8)Colorado State University, Fort Collins, United States, (9)NASA Goddard Institute for Space Studies, New York, United States, (10)NOAA Science Center, North Potomac, MD, United States, (11)NASA Data Science Group, Greenbelt, United States, (12)NASA GSFC, Greenbelt, MD, United States

Sequential Dynamics-aware Foundation Model for Vision-based Space Navigation (1906887)

Woojin Cho¹, Junghwan Park¹ and Darongsae Kwon², (1)TelePIX Inc., AI Research Team, Seoul, South Korea, (2)TelePIX Inc., Seoul, South Korea

Soma: Foundation Model for Multi-Modal Lunar Science using LRO Datasets (1999363)

Daniela Szwarcman¹, Sujit Roy², Vishal Gaur³, Ankur Kumar³, Himanshu Patil³, Gabrielle Nyirjesy⁴, Rachel Slank⁵, Amy Lin⁶, Paolo Fraccaro⁷, Michael K. Barker⁸, Andrew Annex⁹, Zachary Morse¹⁰, Vishnu Viswanathan⁸, Campbell Watson¹¹, Rebekah Dawson-Rigas¹², Manil Maskey¹³, Juan Bernabe-Moreno¹⁴ and Rahul Ramachandran², (1)IBM Research, Rio de Janeiro, Brazil, (2)NASA Marshall Space Flight Center, Huntsville, United States, (3)University of Alabama in Huntsville, Huntsville, United States, (4)IBM Research USA, Yorktown Heights, NY, United States, (5)Universities Space Research Association Huntsville, Huntsville, United States, (6)University of Alabama in Huntsville, Earth System Science Center, Huntsville, United States, (7)IBM Research, Daresbury, United Kingdom, (8)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (9)NASA Ames Research Center, Moffett Field, United States, (10)NASA Goddard Space Flight Center, Greenbelt, United States, (11)IBM Research USA, Yorktown Heights, United States, (12)NASA, Washington, United States, (13)NASA, Huntsville, United States, (14)IBM Research, Mulhuddart, Ireland

Surya: Foundation Model for Heliophysics using SDO Data (1953531)

Sujit Roy¹, Johannes Schmude², Rohit Lal³, Vishal Gaur³, Marcus Freitag², Julian Kuehnert², Ted van Kessel⁴, Johannes Jakubik⁵, Etienne Eben Vos⁶, Kshitiz Mandal³, Joao Lucas Sousa Almeida⁷, Dinesha Vasanta Hegde⁸, Andrés Muñoz-Jaramillo⁹, Amy Lin¹⁰, Talwinder Singh¹¹, Kang Yang¹², Chetraj Pandey¹³, Jinsu Hong¹¹, Berkay Aydin¹⁴, Ryan M McGranaghan¹⁵, Spiridon Kasapis¹⁶, Vishal Upendran¹⁷, Shah Bahauddin¹⁸, Daniel da Silva¹⁹, Nikolai V Pogorelov²⁰, Campbell Watson²¹, Manil Maskey²², Juan Bernabe-Moreno²³ and Rahul Ramachandran²⁴, (1)University of Alabama in Huntsville, Huntsville, AL, United States, (2)IBM Research, Yorktown Heights, United States, (3)University of Alabama in Huntsville, Huntsville, United States, (4)IBM Research USA, Yorktown Heights, United States, (5)IBM Research, Zurich, Switzerland, (6)IBM Research, Johannesburg, South Africa, (7)IBM Research, São Paulo, Brazil, (8)Department of Space Science, University of Alabama in Huntsville, Huntsville, United States, (9)Montana State University, Bozeman, United States, (10)University of Alabama in Huntsville, Earth System Science Center, Huntsville, United States, (11)Georgia State University, Atlanta, GA, United States, (12)Georgia State University, Atlanta, United States, (13)Georgia State University, Computer Science, Atlanta, GA, United States, (14)Georgia State University, Computer Science, Atlanta, United States, (15)NASA Jet Propulsion Laboratory, Pasadena, United States, (16)Princeton University, Department of Astrophysical Sciences, Princeton, United States, (17)SETI Institute Mountain View, Mountain View, United States, (18)Laboratory for Atmospheric and Space Physics, Boulder, CO, United States, (19)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (20)University of Alabama in Huntsville, Department of Space Science, Huntsville, AL, United States, (21)Organization Not Listed, New York, United States, (22)NASA, Huntsville, United States, (23)IBM Research, Mulhuddart, Ireland, (24)NASA Marshall Space Flight Center, Huntsville, United States

Training-Free Scale Adaptation of LVLMs for Remote Sensing Imagery (1912882)

Junhyuk Heo¹, Youkyung Ko¹, Darongsae Kwon¹ and TelePIX, (1)TelePIX Inc., Seoul, South Korea

Uncertainty quantification for foundation models (1890482)

Peter Jan van Leeuwen, Colorado State University, Atmospheric Science, Fort Collins, United States, Christine Chiu, Colorado State University, Fort Collins, United States and Chen Kuang Yang, Colorado State University, Department of Atmospheric Science, Fort Collins, United States

Unlocking the Potential of Satellite Embedding Fields for Landscape Change Attribution (1882087)

Mina Burns¹, Robert E Kennedy¹, Valerie Pasquarella² and Al Kirschbaum³, (1)Oregon State University, Corvallis, OR, United States, (2)Google, Mountain View, United States, (3)National Parks Service, Ashland, United States

Weak Supervision for Mapping Rice Irrigation Practices in South India, Using Diffusion-Based Remote Sensing Foundation Model (1870650)

Rahebe Abedi¹, Nathan Torbick², Preethi Konkathi³, Ishan Ajmera³, Arun Balaji Ramalingam³, Akshay Saini⁴ and Lyndon D Estes⁵, (1)Clark University, Graduate School of Geography, Worcester, MA, United States, (2)Mitti Labs Limited, Lee, United States, (3)Mitti Labs Limited, Mumbai, India, (4)Mitti Labs Limited, _, India, (5)Clark University, Graduate School of Geography, Worcester, United States

Zero-Shot Multi-Spectral Remote Sensing: Repurposing Generalist Multimodal Model Gemini 2.5 for Multi-Spectral Remote Sensing Applications (1898742)

Ganesh Satish Mallya¹, Yotam Gigi², Dahun Kim¹, Maxim Neumann³, Genady Beryozkin², Tomer Shekel² and Anelia Angelova¹, (1)Google DeepMind, Mountain View, United States, (2)Google, Research, Tel Aviv, Israel, (3)Google DeepMind, Zurich, Switzerland

See more of: Informatics

<< Previous Session Proposal | Next Session Proposal >>

IN010 AI Foundation Models for Earth, Space and Planetary Sciences

Session Description:

Co-Sponsor(s):

Index Terms:

IN010
AI Foundation Models for Earth, Space and Planetary Sciences