Landsat-Bench: Datasets and Benchmarks for Landsat Foundation Models
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918053129748480 |
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| author | Corley, Isaac Sharma, Lakshay Crasto, Ruth |
| author_facet | Corley, Isaac Sharma, Lakshay Crasto, Ruth |
| contents | The Landsat program offers over 50 years of globally consistent Earth imagery. However, the lack of benchmarks for this data constrains progress towards Landsat-based Geospatial Foundation Models (GFM). In this paper, we introduce Landsat-Bench, a suite of three benchmarks with Landsat imagery that adapt from existing remote sensing datasets -- EuroSAT-L, BigEarthNet-L, and LC100-L. We establish baseline and standardized evaluation methods across both common architectures and Landsat foundation models pretrained on the SSL4EO-L dataset. Notably, we provide evidence that SSL4EO-L pretrained GFMs extract better representations for downstream tasks in comparison to ImageNet, including performance gains of +4% OA and +5.1% mAP on EuroSAT-L and BigEarthNet-L. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_08780 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Landsat-Bench: Datasets and Benchmarks for Landsat Foundation Models Corley, Isaac Sharma, Lakshay Crasto, Ruth Computer Vision and Pattern Recognition Machine Learning The Landsat program offers over 50 years of globally consistent Earth imagery. However, the lack of benchmarks for this data constrains progress towards Landsat-based Geospatial Foundation Models (GFM). In this paper, we introduce Landsat-Bench, a suite of three benchmarks with Landsat imagery that adapt from existing remote sensing datasets -- EuroSAT-L, BigEarthNet-L, and LC100-L. We establish baseline and standardized evaluation methods across both common architectures and Landsat foundation models pretrained on the SSL4EO-L dataset. Notably, we provide evidence that SSL4EO-L pretrained GFMs extract better representations for downstream tasks in comparison to ImageNet, including performance gains of +4% OA and +5.1% mAP on EuroSAT-L and BigEarthNet-L. |
| title | Landsat-Bench: Datasets and Benchmarks for Landsat Foundation Models |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2506.08780 |