We present an initial evaluation of NASA and IBM's Prithvi-EO-2.0 geospatial foundation model on shoreline delineation of small sandy islands using satellite images. We curated and labeled a dataset of 225 multispectral images of two Maldivian islands, which we publicly release, and fine-tuned both the 300M and 600M parameter versions of Prithvi on training subsets ranging from 5 to 181 images. Our experiments show that even with as few as 5 training images, the models achieve high performance (F1 of 0.94, IoU of 0.79). Our results demonstrate the strong transfer learning capability of Prithvi, underscoring the potential of such models to support coastal monitoring in data-poor regions.

翻译：我们首次评估了NASA与IBM联合开发的Prithvi-EO-2.0地理空间基础模型在卫星影像上对小型沙质岛屿海岸线的划定能力。我们构建并标注了包含两个马尔代夫岛屿的225幅多光谱影像数据集（已公开发布），并使用5至181幅训练子集分别对Prithvi的3亿参数和6亿参数版本进行微调。实验表明，即使仅使用5幅训练图像，模型仍能实现高性能（F1分数0.94，交并比0.79）。我们的结果证明了Prithvi强大的迁移学习能力，凸显了此类模型在数据匮乏区域支持海岸线监测的潜力。