  Vision foundation models are a new frontier in Geospatial Artificial
Intelligence (GeoAI), an interdisciplinary research area that applies and
extends AI for geospatial problem solving and geographic knowledge discovery,
because of their potential to enable powerful image analysis by learning and
extracting important image features from vast amounts of geospatial data. This
paper evaluates the performance of the first-of-its-kind geospatial foundation
model, IBM-NASA's Prithvi, to support a crucial geospatial analysis task: flood
inundation mapping. This model is compared with convolutional neural network
and vision transformer-based architectures in terms of mapping accuracy for
flooded areas. A benchmark dataset, Sen1Floods11, is used in the experiments,
and the models' predictability, generalizability, and transferability are
evaluated based on both a test dataset and a dataset that is completely unseen
by the model. Results show the good transferability of the Prithvi model,
highlighting its performance advantages in segmenting flooded areas in
previously unseen regions. The findings also indicate areas for improvement for
the Prithvi model in terms of adopting multi-scale representation learning,
developing more end-to-end pipelines for high-level image analysis tasks, and
offering more flexibility in terms of input data bands.
