Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917319657127936 |
|---|---|
| author | Szwarcman, Daniela Roy, Sujit Fraccaro, Paolo Gíslason, Þorsteinn Elí Blumenstiel, Benedikt Ghosal, Rinki de Oliveira, Pedro Henrique Almeida, Joao Lucas de Sousa Sedona, Rocco Kang, Yanghui Chakraborty, Srija Wang, Sizhe Gomes, Carlos Kumar, Ankur Truong, Myscon Godwin, Denys Lee, Hyunho Hsu, Chia-Yu Lal, Rohit Asanjan, Ata Akbari Mujeci, Besart Shidham, Disha Keenan, Trevor Arevalo, Paulo Li, Wenwen Alemohammad, Hamed Olofsson, Pontus Hain, Christopher Kennedy, Robert Zadrozny, Bianca Bell, David Cavallaro, Gabriele Watson, Campbell Maskey, Manil Ramachandran, Rahul Moreno, Juan Bernabe |
| author_facet | Szwarcman, Daniela Roy, Sujit Fraccaro, Paolo Gíslason, Þorsteinn Elí Blumenstiel, Benedikt Ghosal, Rinki de Oliveira, Pedro Henrique Almeida, Joao Lucas de Sousa Sedona, Rocco Kang, Yanghui Chakraborty, Srija Wang, Sizhe Gomes, Carlos Kumar, Ankur Truong, Myscon Godwin, Denys Lee, Hyunho Hsu, Chia-Yu Lal, Rohit Asanjan, Ata Akbari Mujeci, Besart Shidham, Disha Keenan, Trevor Arevalo, Paulo Li, Wenwen Alemohammad, Hamed Olofsson, Pontus Hain, Christopher Kennedy, Robert Zadrozny, Bianca Bell, David Cavallaro, Gabriele Watson, Campbell Maskey, Manil Ramachandran, Rahul Moreno, Juan Bernabe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02732 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications Szwarcman, Daniela Roy, Sujit Fraccaro, Paolo Gíslason, Þorsteinn Elí Blumenstiel, Benedikt Ghosal, Rinki de Oliveira, Pedro Henrique Almeida, Joao Lucas de Sousa Sedona, Rocco Kang, Yanghui Chakraborty, Srija Wang, Sizhe Gomes, Carlos Kumar, Ankur Truong, Myscon Godwin, Denys Lee, Hyunho Hsu, Chia-Yu Lal, Rohit Asanjan, Ata Akbari Mujeci, Besart Shidham, Disha Keenan, Trevor Arevalo, Paulo Li, Wenwen Alemohammad, Hamed Olofsson, Pontus Hain, Christopher Kennedy, Robert Zadrozny, Bianca Bell, David Cavallaro, Gabriele Watson, Campbell Maskey, Manil Ramachandran, Rahul Moreno, Juan Bernabe Computer Vision and Pattern Recognition 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. |
| title | Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.02732 |