Summary of Damage Assessment After Natural Disasters with Uavs: Semantic Feature Extraction Using Deep Learning, by Nethmi S. Hewawiththi et al.
Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning
by Nethmi S. Hewawiththi, M. Mahesha Viduranga, Vanodhya G. Warnasooriya, Tharindu Fernando, Himal A. Suraweera, Sridha Sridharan, Clinton Fookes
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a novel semantic extractor for machine learning algorithms running onboard unmanned aerial vehicles (UAVs) to enhance disaster recovery missions. The extractor identifies critical data required for decision-making, reducing the amount of data that needs to be transmitted to ground stations due to limited bandwidth and intermittent connectivity. The proposed architecture is tested on two publicly available datasets, FloodNet and RescueNet, for visual question answering and disaster damage level classification tasks. Results demonstrate high accuracy across different downstream tasks while significantly reducing data transmission volume. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps UAVs with machine learning work better in disaster recovery missions. It’s hard to send big amounts of data from the air to the ground because it’s slow and unreliable. The solution is a new tool that finds important information and sends only that, saving time and resources. The tool is tested on real-world examples and works well for two tasks: answering questions about what happened during an event and predicting the severity of damage. |
Keywords
» Artificial intelligence » Classification » Machine learning » Question answering
