Vision foundation models are a new frontier in GeoAI research 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 popular 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 impressive transferability
of the Prithvi model, highlighting its performance advantages in segmenting
flooded areas in previously unseen regions. The findings also suggest 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.

评估 IBM-NASA 的 Prithvi 模型在洪水淹没区域的地理空间分析任务中的性能，并与其他卷积神经网络和视觉变换器架构进行比较，结果显示了 Prithvi 模型在未见过的区域中分割淹没区域的出色可迁移性，同时建议在多尺度表示学习、高级图像分析任务的端到端流程和输入数据波段的灵活性方面进一步改进。