Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications
About
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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Semantic segmentation | Sen1Floods11 | mIoU (macro)87.12 | 29 | |
| Pixel-wise classification | Dominant Leaf Type Area of interest A+ | IoU80 | 26 | |
| Semantic segmentation | MADOS | mIoU41.46 | 26 | |
| Semantic segmentation | HLS Burn Scars | mIoU80.28 | 25 | |
| Semantic segmentation | PASTIS | Macro mIoU31.12 | 24 | |
| Semantic segmentation | SN-7-TS (test) | mIoU56.54 | 24 | |
| Segmentation | m-chesapeake | Mean mIoU69.19 | 23 | |
| Crop type classification | Sen4Map original (test) | Weighted F1 Score84.6 | 20 | |
| Land Cover Classification | Sen4Map (test) | Weighted F176.1 | 20 | |
| Semantic segmentation | HLS Burns (test) | mIoU83.62 | 19 |