IEEE Transactions on Geoscience and Remote Sensing· 2025Q1
Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications
- 99citations
- Q1SCImago
- 2025year
Short summary
Prithvi-EO-2.0, a new geospatial foundation model trained on 4.2 million global time series samples, outperforms its predecessor by 8% and six other models across diverse remote sensing tasks.
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Key points
- Prithvi-EO-2.0 is a new geospatial foundation model trained on 4.2 million global time series samples at 30-m resolution.
- The model incorporates temporal and location embeddings for enhanced performance.
- It outperforms its predecessor (Prithvi-EO-1.0) by 8% and six other geospatial foundation models.
- The model demonstrates versatility across classical Earth observation and high-resolution applications (0.1 m to 15 m).
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. IEEE Transactions on Geoscience and Remote Sensing, 2025 · DOI ↗
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