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Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications

Daniela Szwarcman, Sujit Roy, Paolo Fraccaro, Orsteinn Elí Gíslason, Benedikt Blumenstiel, Rinki Ghosal, Pedro Henrique de Oliveira, J. Almeida, Rocco Sedona, Yanghui Kang, Srija Chakraborty, Sizhe Wang, Carlos Augusto Gomes, Ankur Kumar, Vishal Gaur, Myscon Truong, Denys Godwin, Sam Khallaghi, Hyunho Lee, Chia-Yu Hsu, Ata Akbari Asanjan, Besart Mujeci, Disha Shidham, Rufai Omowunmi Balogun, Venkatesh Kolluru, Trevor F. Keenan, Paulo Arévalo, Wenwen Li, Hamed Alemohammad, Pontus Olofsson, Timothy Mayer, Christopher Hain, Robert T. Kennedy, Bianca Zadrozny, David A. Bell, Gabriele Cavallaro, Campbell Watson, Manil Maskey, Rahul Ramachandran, Juan Bernabe Moreno

2025IEEE Transactions on Geoscience and Remote Sensing24 citationsDOIOpen Access PDF

Abstract

This paper presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2 million global time series samples from NASA’s Harmonized Landsat and Sentinel-2 data archive at 30-m resolution, the new model incorporates temporal and location embeddings for enhanced performance across various geospatial tasks. Through extensive benchmarking with GEO-Bench, the model 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 0.1 m to 15 m). 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) allowed constant feedback on model and dataset design, enabling customization across diverse SME-led applications in disaster response, land cover and crop mapping, and ecosystem dynamics monitoring. Prithvi-EO-2.0 is available as an open-source model on Hugging Face and IBM TerraTorch, with additional resources on GitHub. The project exemplifies the Trusted Open Science approach embraced by all involved organizations.

Topics & Concepts

Geospatial analysisComputer scienceEarth observationRemote sensingBenchmarkingLand coverData scienceIBMDigital EarthData modelingData miningRange (aeronautics)GeoinformaticsGeomaticsGeographic information systemPersonalizationDigital elevation modelTerrainFace (sociological concept)Foundation (evidence)Frame (networking)Earth scienceThematic mapSystems engineeringRemote Sensing in AgricultureRemote-Sensing Image ClassificationGeographic Information Systems Studies
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