  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}.
