Summary of Enabling Quick, Accurate Crowdsourced Annotation For Elevation-aware Flood Extent Mapping, by Landon Dyken et al.
Enabling Quick, Accurate Crowdsourced Annotation for Elevation-Aware Flood Extent Mapping
by Landon Dyken, Saugat Adhikari, Pravin Poudel, Steve Petruzza, Da Yan, Will Usher, Sidharth Kumar
First submitted to arxiv on: 31 Jul 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 The paper introduces FloodTrace, an application that enables effective crowdsourcing for flooded region annotation in machine learning training data. This reduces the need for researchers to solely annotate flood extent mappings. The app utilizes elevation-guided annotation tools and 3D rendering to inform user decisions with digital elevation model data, improving accuracy. It also provides a framework for reviewing and correcting inaccuracies using uncertainty visualization-inspired methods. The application was tested in a user study involving 266 graduate students annotating high-resolution aerial imagery from Hurricane Matthew in North Carolina. Results show the app’s accuracy and efficiency benefits even for untrained users. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FloodTrace is an app that helps people create maps of flood damage after disasters happen. It uses special tools to help people label areas on satellite images where flooding occurred, so computer models can learn from this information. This makes it easier to predict future floods and prepare for them. The app also has a way for experts to review the work done by regular people and fix any mistakes. |
Keywords
* Artificial intelligence * Machine learning
