Summary of Flooddamagecast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation, by Chia-fu Liu et al.
FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation
by Chia-Fu Liu, Lipai Huang, Kai Yin, Sam Brody, Ali Mostafavi
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper introduces FloodDamageCast, a machine learning framework for near-real-time estimation of building and infrastructure damage during disaster response. The framework uses heterogeneous data to predict residential flood damage at a 500×500 meter resolution in Harris County, Texas, during Hurricane Harvey. To address data imbalance, the model incorporates generative adversarial networks-based data augmentation and an efficient machine learning approach. Results show that FloodDamageCast can identify high-damage areas missed by baseline models. The study’s insights can aid emergency responders in allocating resources efficiently, reducing time and effort spent on inspections. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FloodDamageCast is a new way to predict damage to buildings after floods happen. It uses a special type of artificial intelligence called machine learning to figure out how much damage happened during Hurricane Harvey in Texas. The team used a lot of different kinds of data, like pictures and information about the floodwaters. They then used that data to make a map showing where the most damage occurred. This can help emergency responders know where to focus their efforts when cleaning up after a disaster. |
Keywords
» Artificial intelligence » Data augmentation » Machine learning
