利用Segment Anything模型从卫星影像中识别难民营建筑（SAM4Refugee）以支持人道主义行动
Leveraging Segment Anything Model in Identifying Buildings within Refugee Camps (SAM4Refugee) from Satellite Imagery for Humanitarian Operations
AI总结：
本研究利用SAM及其轻量级适配版本SAM-Adapter从卫星影像中提取难民营建筑，在数据有限时优于U-Net和Transformer，并发现超分辨率放大可提升性能及加速收敛，代码已开源。
摘要：
从高分辨率卫星影像中更新难民营的建筑足迹可以支持相关的人道主义行动。本研究探讨了“Segment Anything Model”（SAM）及其分支之一SAM-Adapter在从卫星影像中提取建筑的语义分割任务中的应用。SAM-Adapter是SAM的轻量级适配版本，并成为在不同难民营中执行该提取任务的强大工具。我们的研究证明，在数据可用性有限的场景下，与其他经典（如U-Net）或先进的语义分割模型（如Transformer）相比，SAM-Adapter表现出色。此外，研究强调了放大技术对模型性能的影响，其中超分辨率（SR）模型等方法对提升模型性能具有不可估量的价值。研究还揭示了有趣的现象，包括当使用放大后的图像数据进行训练时，模型在第一个训练周期内快速收敛，这为未来研究提供了机会。涵盖从数据准备、模型训练、模型推理到生成预测掩膜的Shapefile的每一步代码均可在GitHub仓库中获取，以惠及更广泛的科学界和人道主义行动。
英文摘要：
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.
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