Summary of Crasar-u-droids: a Large Scale Benchmark Dataset For Building Alignment and Damage Assessment in Georectified Suas Imagery, by Thomas Manzini et al.
CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery
by Thomas Manzini, Priyankari Perali, Raisa Karnik, Robin Murphy
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Center for Robot Assisted Search And Rescue (CRASAR) has created a new dataset, CRASAR-U-DROIDs, which aims to improve machine learning and computer vision models for disaster response. The dataset consists of high-resolution geospatial images from small uncrewed aerial systems (sUAS), collected from ten federally declared disasters. These images cover an area of 67.98 square kilometers and contain over 21,000 building polygons with damage labels. Additionally, the dataset includes spatial alignment annotations to enable more accurate machine learning models. The CRASAR-U-DROIDs dataset is unique in that it is the largest labeled dataset of sUAS orthomosaic imagery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Center for Robot Assisted Search And Rescue has made a new tool to help robots find damage after big disasters like hurricanes and fires. They used special cameras on small flying machines called drones to take pictures of the areas affected. They took over 20,000 pictures that show where buildings are damaged. This will help computers learn how to quickly identify which buildings need repair or rebuilding. |
Keywords
» Artificial intelligence » Alignment » Machine learning
