  When we are primarily interested in solving several problems jointly with a
given prescribed high performance accuracy for each target application, then
Foundation Models should for most cases be used rather than problem-specific
models. We focus on the specific Computer Vision application of Foundation
Models for Earth Observation (EO) and geospatial AI. These models can solve
important problems we are tackling, including for example land cover
classification, crop type mapping, flood segmentation, building density
estimation, and road regression segmentation. In this paper, we show that for a
limited number of labelled data, Foundation Models achieve improved performance
compared to problem-specific models. In this work, we also present our proposed
evaluation benchmark for Foundation Models for EO. Benchmarking the
generalization performance of Foundation Models is important as it has become
difficult to standardize a fair comparison across the many different models
that have been proposed recently. We present the results using our evaluation
benchmark for EO Foundation Models and show that Foundation Models are label
efficient in the downstream tasks and help us solve problems we are tackling in
EO and remote sensing.
