TY  - JOUR
AU  - Szwarcman, Daniela
AU  - Roy, Sujit
AU  - Fraccaro, Paolo
AU  - Gíslason, Orsteinn Elí
AU  - Blumenstiel, Benedikt
AU  - Ghosal, Rinki
AU  - De Oliveira, Pedro Henrique
AU  - Almeida, Joao Lucas de Sousa
AU  - Sedona, Rocco
AU  - Kang, Yanghui
AU  - Chakraborty, Srija
AU  - Wang, Sizhe
AU  - Gomes, Carlos
AU  - Kumar, Ankur
AU  - Gaur, Vishal
AU  - Truong, Myscon
AU  - Godwin, Denys
AU  - Khallaghi, Sam
AU  - Lee, Hyunho
AU  - Hsu, Chia-Yu
AU  - Asanjan, Ata Akbari
AU  - Mujeci, Besart
AU  - Shidham, Disha
AU  - Balogun, Rufai Omowunmi
AU  - Kolluru, Venkatesh
AU  - Keenan, Trevor
AU  - Arevalo, Paulo
AU  - Li, Wenwen
AU  - Alemohammad, Hamed
AU  - Olofsson, Pontus
AU  - Mayer, Timothy
AU  - Hain, Christopher
AU  - Kennedy, Robert
AU  - Zadrozny, Bianca
AU  - Bell, David
AU  - Cavallaro, Gabriele
AU  - Watson, Campbell
AU  - Maskey, Manil
AU  - Ramachandran, Rahul
AU  - Moreno, Juan Bernabe
TI  - Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications
JO  - IEEE transactions on geoscience and remote sensing
VL  - 64
SN  - 0018-9413
CY  - New York, NY
PB  - IEEE
M1  - FZJ-2025-05777
SP  -  4400120 
PY  - 2025
AB  - 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.
LB  - PUB:(DE-HGF)16
DO  - DOI:10.1109/TGRS.2025.3642610
UR  - https://juser.fz-juelich.de/record/1050065
ER  -