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Cited by 41Remote-Sensing Image ClassificationRemote Sensing in AgricultureAdvanced Neural Network Applications

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GEO-Bench: Toward Foundation Models for Earth Monitoring

Open access
Lacoste, Alexandre, Lehmann, Nils, Rodriguez, Pau, Sherwin, Evan, Kerner, Hannah, Lütjens, Björn, Irvin, Jeremy, Dao, David, Alemohammad, Hamed, Drouin, Alexandre, Gunturkun, Mehmet, Huang, Gabriel, Vazquez, David, Newman, Dava, Bengio, Yoshua, Ermon, Stefano, Zhu, Xiaoxiang·2023·Advances in Neural Information Processing Systems 36
DOI 10.52202/075280-2223
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Abstract

Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.

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  • The GEO-Bench evaluation protocol begins with hyperparameter tuning (ten trials per dataset), then uses the best hyperparameters for repeated experiments (N=10) with different random seeds.

    “This protocol begins with hyperparameter tuning, where each model is allowed a fixed number of trials (ten in our study) on each dataset. The best hyperparameters identified on the validation set for each task are then used to conduct repeated experiments (N = 10) with different random seeds.”

    From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [10]

    Section 6.1 (Protocol; page unavailable) says the authors selected hyperparameters using validation performance over 16 trials, then repeated training with 10 seeds. Section 4 recommends a maximum of 16 tuning trials per task. The sequence and seed count are supported, but the attributed ten-trial tuning count is not. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

  • GEO-Bench is a widely used and rigorous benchmark framework available for EO foundation models.

    “For the benchmarking evaluation, we selected GEO-Bench [10], a widely used and rigorous benchmark framework available for EO foundation models.”

    From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [10]

    Sections 3 and 4.1 describe GEO-Bench as a benchmark for evaluating Earth-monitoring foundation models, with diverse tasks and procedures for reporting reliable confidence intervals. These support its availability and methodological rigor, but the paper does not establish that GEO-Bench is widely used. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Through benchmarking with GEO-Bench, Prithvi-EO-2.0 outperforms the previous Prithvi-EO model by 8% across a range of tasks.

    “Through extensive benchmarking with GEO-Bench, the model outperforms the previous PrithviEO model by 8% across a range of tasks.”

    From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [10]

    Section 3 identifies GEO-Bench as a benchmark with six classification and six segmentation tasks, supporting its use as a benchmark across tasks. The experiments passage (Section 6.2.2; page unavailable) discusses other model baselines, but our copy does not report a Prithvi-EO-2.0 versus Prithvi-EO comparison or an 8% improvement; searches for those models and the figure found none. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • GEO-Bench offers a carefully curated collection of six classification and six semantic segmentation datasets, encompassing a range of spatial resolutions and domains.

    “GEO-Bench offers a carefully curated collection of six classification and six semantic segmentation datasets [10], encompassing a range of spatial resolutions and domains (see Table I).”

    From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [10]

  • Prithvi-EO-2.0 showed strong performance in standard benchmarking experiments at different resolutions, improving up to 8% over its predecessor and outperforming some of the most popular GFMs on GEO-Bench datasets.

    “Prithvi-EO-2.0 showed strong performance in both standard benchmarking experiments at different resolutions, where it improved up-to 8% its predecessor and outperformed some of the most popular GFMs on GEOBench datasets [10].”

    From Prithvi-EO-2.0: A Versatile Multitemporal Foundation Model for Earth Observation Applications (2026) · cites this paper as [10]

    The Introduction and §3 describe GEO-Bench as a benchmark, and the Experiments section reports results for other model baselines. Searches for Prithvi-EO-2.0 and an 8% improvement found no passage in our copy addressing its performance, predecessor comparison, or results at different resolutions. Page numbers were not available in the indexed passages. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Metadata

Authors
Lacoste, A., Lehmann, N., Rodriguez, P., Sherwin, E., Kerner, H., Lütjens, B., Irvin, J., Dao, D., Alemohammad, H., Drouin, A., Gunturkun, M., Huang, G., Vazquez, D., Newman, D., Bengio, Y., Ermon, S., & Zhu, X.
ORCID
Venue
Advances in Neural Information Processing Systems 36
Publisher
Neural Information Processing Systems Foundation, Inc. (NeurIPS)
Published
2023
Pages
51080-51093
ISSN
1049-5258 (ISSN-L)
DOI
10.52202/075280-2223
OpenAlex
W7133235565
Cited by
40
References
0
Citations by year
2026: 20 · 2025: 9 · 2024: 11
FWCI
15.68
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • neural information processing systems (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • Clark Digital Commons (Clark University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
  • Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
foundation models, Earth observation, geospatial data, benchmark, large language models

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