  The Landsat program is the longest-running Earth observation program in
history, with 50+ years of data acquisition by 8 satellites. The multispectral
imagery captured by sensors onboard these satellites is critical for a wide
range of scientific fields. Despite the increasing popularity of deep learning
and remote sensing, the majority of researchers still use decision trees and
random forests for Landsat image analysis due to the prevalence of small
labeled datasets and lack of foundation models. In this paper, we introduce
SSL4EO-L, the first ever dataset designed for Self-Supervised Learning for
Earth Observation for the Landsat family of satellites (including 3 sensors and
2 product levels) and the largest Landsat dataset in history (5M image
patches). Additionally, we modernize and re-release the L7 Irish and L8 Biome
cloud detection datasets, and introduce the first ML benchmark datasets for
Landsats 4-5 TM and Landsat 7 ETM+ SR. Finally, we pre-train the first
foundation models for Landsat imagery using SSL4EO-L and evaluate their
performance on multiple semantic segmentation tasks. All datasets and model
weights are available via the TorchGeo (https://github.com/microsoft/torchgeo)
library, making reproducibility and experimentation easy, and enabling
scientific advancements in the burgeoning field of remote sensing for a
multitude of downstream applications.
