{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/prithvi-eo-2-0-a-versatile-multi-temporal","title":"Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications","arxiv_id":"2412.02732","date":"2024-12-03","proceeding":null,"authors":["Daniela Szwarcman","Sujit Roy","Paolo Fraccaro","Þorsteinn Elí Gíslason","Benedikt Blumenstiel","Rinki Ghosal","Pedro Henrique de Oliveira","Joao Lucas de Sousa Almeida","Rocco Sedona","Yanghui Kang","Srija Chakraborty","Sizhe Wang","Carlos Gomes","Ankur Kumar","Myscon Truong","Denys Godwin","Hyunho Lee","Chia-Yu Hsu","Ata Akbari Asanjan","Besart Mujeci","Disha Shidham","Trevor Keenan","Paulo Arevalo","Wenwen Li","Hamed Alemohammad","Pontus Olofsson","Christopher Hain","Robert Kennedy","Bianca Zadrozny","David Bell","Gabriele Cavallaro","Campbell Watson","Manil Maskey","Rahul Ramachandran","Juan Bernabe Moreno"],"abstract":"This technical report presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2M global time series samples from NASA's Harmonized Landsat and Sentinel-2 data archive at 30m resolution, the new 300M and 600M parameter models incorporate temporal and location embeddings for enhanced performance across various geospatial tasks. Through extensive benchmarking with GEO-Bench, the 600M version 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 0.1m to 15m). 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) are among the key factors that contributed to the project's success. In particular, SME involvement allowed for constant feedback on model and dataset design, as well as successful customization for diverse SME-led applications in disaster response, land use and crop mapping, and ecosystem dynamics monitoring. Prithvi-EO-2.0 is available on Hugging Face and IBM terratorch, with additional resources on GitHub. The project exemplifies the Trusted Open Science approach embraced by all involved organizations.","url_abs":"https://arxiv.org/abs/2412.02732v2","url_pdf":"https://arxiv.org/pdf/2412.02732v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"prithvi-eo-2-0-a-versatile-multi-temporal","repo_url":"https://github.com/NASA-IMPACT/Prithvi-EO-2.0","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"disaster-response","task_name":"Disaster Response"},{"task_slug":"earth-observation","task_name":"Earth Observation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2412.02732","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.02732"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/NASA-IMPACT/Prithvi-EO-2.0","reach":null}],"summary":{"ran_honours":2},"by_repo_kind":{"official":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"6cf26d0a1f2e29dc","entry":"get_mae","repo":"NASA-IMPACT/Prithvi-EO-2.0","repo_kind":"official","path":"examples/carbon_flux/main_flux_finetune_baselines_trainer.py","file_url":"https://github.com/NASA-IMPACT/Prithvi-EO-2.0/blob/HEAD/examples/carbon_flux/main_flux_finetune_baselines_trainer.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6cf26d0a1f2e29dc"}},{"code_sha256_prefix":"f53e32da443676a7","entry":"get_mse","repo":"NASA-IMPACT/Prithvi-EO-2.0","repo_kind":"official","path":"examples/carbon_flux/main_flux_finetune_baselines_trainer.py","file_url":"https://github.com/NASA-IMPACT/Prithvi-EO-2.0/blob/HEAD/examples/carbon_flux/main_flux_finetune_baselines_trainer.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f53e32da443676a7"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}