  Updated building footprints with refugee camps from high-resolution satellite
imagery can support related humanitarian operations. This study explores the
utilization of the "Segment Anything Model" (SAM) and one of its branches,
SAM-Adapter, for semantic segmentation tasks in the building extraction from
satellite imagery. SAM-Adapter is a lightweight adaptation of the SAM and
emerges as a powerful tool for this extraction task across diverse refugee
camps. Our research proves that SAM-Adapter excels in scenarios where data
availability is limited compared to other classic (e.g., U-Net) or advanced
semantic segmentation models (e.g., Transformer). Furthermore, the impact of
upscaling techniques on model performance is highlighted, with methods like
super-resolution (SR) models proving invaluable for improving model
performance. Additionally, the study unveils intriguing phenomena, including
the model's rapid convergence in the first training epoch when using upscaled
image data for training, suggesting opportunities for future research. The
codes covering each step from data preparation, model training, model
inferencing, and the generation of Shapefiles for predicted masks are available
on a GitHub repository to benefit the extended scientific community and
humanitarian operations.
