FMARS：利用基础模型为灾害管理标注遥感图像
FMARS: Annotating Remote Sensing Images for Disaster Management using Foundation Models
AI总结：
针对甚高分辨率遥感图像标注不足的问题，提出FMARS方法，结合基础模型自动生成灾害管理相关标注，构建大规模灾前图像数据集并通过无监督域适应提升分割模型迁移性，验证了基础模型规模化标注遥感数据的有效性。
摘要：
甚高分辨率（VHR）遥感图像的获取日益便捷，但往往缺乏有效机器学习应用所需的标注。近期的基础模型，如GroundingDINO和Segment Anything（SAM，分割一切模型），为自动生成标注提供了可能。本研究提出FMARS（Foundation Model Annotations in Remote Sensing，遥感基础模型标注）方法，该方法利用VHR图像和基础模型实现快速、鲁棒的标注。我们聚焦灾害管理领域，基于Maxar开放数据计划，提供了一个大规模数据集，其标签来自19起灾害事件的灾前图像。我们利用生成的标签训练分割模型，并采用无监督域适应（UDA）技术提升模型在真实场景中的迁移能力。研究结果证明，利用基础模型大规模自动标注遥感数据是有效的，可为关键应用构建鲁棒的下游模型。代码和数据集可在https://github.com/links-ads/igarss-fmars 获取。
英文摘要：
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}.
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