Very-High Resolution (VHR) remote sensing imagery is increasingly accessible, but often lacks annotations for effective machine learning applications. Recent foundation models like GroundingDINO and Segment Anything (SAM) provide opportunities to automatically generate annotations. This study introduces FMARS (Foundation Model Annotations in Remote Sensing), a methodology leveraging VHR imagery and foundation models for fast and robust annotation. We focus on disaster management and provide a large-scale dataset with labels obtained from pre-event imagery over 19 disaster events, derived from the Maxar Open Data initiative. We train segmentation models on the generated labels, using Unsupervised Domain Adaptation (UDA) techniques to increase transferability to real-world scenarios. Our results demonstrate the effectiveness of leveraging foundation models to automatically annotate remote sensing data at scale, enabling robust downstream models for critical applications. Code and dataset are available at \url{https://github.com/links-ads/igarss-fmars}.

翻译：超高分辨率（VHR）遥感影像日益普及，但通常缺乏用于有效机器学习应用的标注信息。近期的基础模型，如GroundingDINO和Segment Anything（SAM），为自动生成标注提供了可能。本研究提出了FMARS（遥感基础模型标注），这是一种利用VHR影像和基础模型进行快速、鲁棒标注的方法。我们聚焦于灾害管理，并提供了一个大规模数据集，其标注来源于Maxar开放数据计划中19次灾害事件的事前影像。我们利用生成的标注训练分割模型，并采用无监督域自适应（UDA）技术以增强模型向真实场景的迁移能力。我们的结果证明了利用基础模型大规模自动标注遥感数据的有效性，从而能够为关键应用构建鲁棒的下游模型。代码和数据集可在 \url{https://github.com/links-ads/igarss-fmars} 获取。