构建首个兰斯卫星影像基准测试，推动地球观测大模型发展
Landsat-Bench: Datasets and Benchmarks for Landsat Foundation Models
- 基于三个遥感数据集改造出兰斯卫星基准任务
- 自监督预训练模型在分类任务上比ImageNet提升4%准确率
- 适合遥感图像分析与地球观测大模型研究者使用
兰斯卫星计划提供了超过50年的全球一致地球影像。然而，缺乏针对该数据的基准测试，制约了基于兰斯影像的地理空间基础模型（GFM）的发展。本文提出Landsat-Bench，一套包含三个基准任务的评测体系，其数据源自EuroSAT-L、BigEarthNet-L和LC100-L等现有遥感数据集。我们建立了统一的评估方法，涵盖常见架构与在SSL4EO-L数据集上预训练的兰斯基础模型。结果表明，相较于ImageNet预训练模型，SSL4EO-L预训练的GFM在下游任务中提取表征能力更强，在EuroSAT-L和BigEarthNet-L上分别实现+4%总体准确率和+5.1%平均精度（mAP）的提升。
原文摘要 · Abstract (English)
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.
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