Landsat-Bench: Datasets and Benchmarks for Landsat Foundation Models

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.

Robustness to Geographic Distribution Shift using Location Encoders

Geographic distribution shift arises when the distribution of locations on Earth in a training dataset is different from what is seen at test time. The most common approaches to tackling geographic distribution shift treat regions delimited by administrative boundaries such as countries or continents as separate domains and apply standard domain adaptation methods, ignoring geographic coordinates that are often available as metadata. This paper proposes the use of location encoders for training models that are more robust to geographic distribution shift. We show how both simple sine-cosine encoders and pre-trained location encoders can be used to improve standard domain adaptation methods for the special case of geographic distribution shift. Our proposed methods achieve state-of-the-art results on geo-tagged imagery datasets from the WILDS benchmark.