  Access to high resolution satellite imagery has dramatically increased in
recent years as several new constellations have entered service. High revisit
frequencies as well as improved resolution has widened the use cases of
satellite imagery to areas such as humanitarian relief and even Search and
Rescue (SaR). We propose a novel remote sensing object detection dataset for
deep learning assisted SaR. This dataset contains only small objects that have
been identified as potential targets as part of a live SaR response. We
evaluate the application of popular object detection models to this dataset as
a baseline to inform further research. We also propose a novel object detection
metric, specifically designed to be used in a deep learning assisted SaR
setting.
