陆地卫星计划提供了超过50年的全球一致的地球影像数据。然而，该数据缺乏相应的基准测试，制约了基于陆地卫星的地理空间基础模型（GFM）的发展。本文介绍了Landsat-Bench，一套包含三个基准测试的工具集，其使用陆地卫星影像并基于现有的遥感数据集进行改编——EuroSAT-L、BigEarthNet-L和LC100-L。我们在通用架构及在SSL4EO-L数据集上预训练的陆地卫星基础模型上建立了基线和标准化的评估方法。值得注意的是，我们提供了证据表明，相较于ImageNet预训练的基础模型，SSL4EO-L预训练的GFM在下游任务中提取的表征更为优越，在EuroSAT-L和BigEarthNet-L上的总体准确率（OA）提升达+4%，平均精度均值（mAP）提升达+5.1%。

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.