SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite Imagery
This work addresses the challenge of improving efficiency in humanitarian relief and SaR missions through deep learning, but it is incremental as it builds on existing object detection methods with a new dataset and metric.
The authors tackled the problem of automating search and rescue (SaR) operations using satellite imagery by creating a novel dataset focused on small objects identified during live responses, and they evaluated popular object detection models as a baseline while proposing a new metric tailored for this setting.
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.