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Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation ApplicationsThis technical report presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2M global time series samples from NASA’s Harmonized Landsat and Sentinel-2 data archive at 30m resolution, the new 300M and 600M parameter
models incorporate temporal and location embeddings for enhanced performance across various geospatial tasks. Through extensive benchmarking with GEOBench, the 600M version outperforms the previous Prithvi-EO model by 8% across a range of tasks. It also outperforms six other geospatial foundation models when benchmarked on remote sensing tasks from different domains and resolutions (i.e. from 0.1m to 15m). The results demonstrate the versatility of the model in both classical earth observation and high-resolution applications. Early involvement of end-users and subject matter experts (SMEs) are among the key factors that contributed to the project’s success. In particular, SME involvement allowed for constant feedback on model and dataset design, as well as successful customization for diverse SME-led applications in disaster response, land use and crop mapping, and ecosystem dynamics monitoring. Prithvi-EO-2.0 is available on Hugging Face and IBM terratorch, with additional resources on GitHub. The project exemplifies the Trusted Open Science approach embraced by all involved organizations.
Document ID
20240015391
Acquisition Source
Marshall Space Flight Center
Document Type
Preprint (Draft being sent to journal)
Authors
Daniela Szwarcman
(IBM Research)
Sujit Roy
(University of Alabama in Huntsville Huntsville, United States)
Paolo Fraccaro
(IBM Research)
Þorsteinn Elí Gíslason
(University of Iceland Reykjavik, Iceland)
Benedikt Blumenstiel
(IBM Research)
Rinki Ghosal
(University of Alabama in Huntsville Huntsville, United States)
Pedro Henrique de Oliveira
(IBM Research)
Joao Lucas de Sousa Almeida
(IBM Research)
Rocco Sedona
(Jülich Supercomputing Centre Forschungszentrum Jülich, Jülich, DE)
Yanghui Kang
(Jülich Supercomputing Centre Forschungszentrum Jülich, Jülich, DE)
Srija Chakraborty
(Universities Space Research Association Huntsville, AL, United States)
Sizhe Wang
(Arizona State Univ. Tempe, AZ, United States)
Ankur Kumar
(University of Alabama in Huntsville Huntsville, United States)
Hyunho Lee
(Arizona State University Tempe, United States)
Chia-Yu Hsu
(Arizona State University Tempe, United States)
Ata Akbari Asanjan
(Universities Space Research Association Huntsville, AL, United States)
Besart Mujeci
(Universities Space Research Association Huntsville, AL, United States)
Trevor Keenan
(University of California, Berkeley Berkeley, United States)
Paulo Arevalo
(Boston Univ. MA, United States)
Wenwen Li
(Arizona State Univ. Tempe, AZ, United States)
Hamed Alemohammad
(Clark University Worcester, United States)
Pontus Olofsson
(Marshall Space Flight Center Redstone Arsenal, United States)
Christopher R Hain
(Marshall Space Flight Center Redstone Arsenal, United States)
Robert Kennedy
(Oregon State University Corvallis, United States)
Bianca Zadrozny
(IBM Research)
Gabriele Cavallaro
(University of Iceland Reykjavik, Iceland)
Campbell Watson
(IBM Research Yorktown Heights, New York, United States)
Manil Maskey
(Marshall Space Flight Center Redstone Arsenal, United States)
Rahul Ramachandran
(Marshall Space Flight Center Redstone Arsenal, United States)
Juan Antonio Bernabe Moreno
(IBM Research - Ireland Dublin, Ireland)
Date Acquired
December 2, 2024
Publication Date
December 17, 2024
Publication Information
Publication: arXiv
Publisher: Cornell University
URL: https://arxiv.org/
Subject Category
Earth Resources and Remote Sensing
Funding Number(s)
CONTRACT_GRANT: 80MSFC22M0004
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Technical Review
Single Expert
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