Summary of Automated Floodwater Depth Estimation Using Large Multimodal Model For Rapid Flood Mapping, by Temitope Akinboyewa et al.
Automated Floodwater Depth Estimation Using Large Multimodal Model for Rapid Flood Mapping
by Temitope Akinboyewa, Huan Ning, M. Naser Lessani, Zhenlong Li
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed research presents an innovative approach to estimating floodwater depth using on-site flood photos. A pre-trained multimodal model, GPT-4 Vision, is utilized to estimate floodwater depth from flooding photos containing referenced objects such as street signs, cars, people, and buildings. The model returns the floodwater depth by utilizing the heights of common objects as references. This rapid estimation approach provides consistent and reliable results, revolutionizing flood inundation mapping and enabling near-real-time assessments of flood severity, which is crucial for effective response strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to figure out how deep the water is after a big flood. Usually, people have to go out and take pictures or use special machines, but that takes time and resources. A team of researchers came up with a new way to do it using just photos taken from the scene. They used a special model that can look at pictures and figure out how deep the water is based on things in the photo like street signs, cars, people, and buildings. This makes it much faster and easier to get an idea of how bad the flood is, which is really important for helping people affected by floods. |
Keywords
» Artificial intelligence » Gpt
