  Massive amounts of unlabelled data are captured by Earth Observation (EO)
satellites, with the Sentinel-2 constellation generating 1.6 TB of data daily.
This makes Remote Sensing a data-rich domain well suited to Machine Learning
(ML) solutions. However, a bottleneck in applying ML models to EO is the lack
of annotated data as annotation is a labour-intensive and costly process. As a
result, research in this domain has focused on Self-Supervised Learning and
Foundation Model approaches. This paper addresses the need to evaluate
different Foundation Models on a fair and uniform benchmark by introducing the
PhilEO Bench, a novel evaluation framework for EO Foundation Models. The
framework comprises of a testbed and a novel 400 GB Sentinel-2 dataset
containing labels for three downstream tasks, building density estimation, road
segmentation, and land cover classification. We present experiments using our
framework evaluating different Foundation Models, including Prithvi and SatMAE,
at multiple n-shots and convergence rates.
