Summary of Lessons From a Human-in-the-loop Machine Learning Approach For Identifying Vacant, Abandoned, and Deteriorated Properties in Savannah, Georgia, by Xiaofan Liang et al.
Lessons from a human-in-the-loop machine learning approach for identifying vacant, abandoned, and deteriorated properties in Savannah, Georgia
by Xiaofan Liang, Brian Brainerd, Tara Hicks, Clio Andris
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Physics and Society (physics.soc-ph)
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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 A novel machine learning approach, called VADecide, is presented that tackles the challenge of identifying vacant, abandoned, and deteriorated (VAD) properties in urban areas. By incorporating human input into the model’s training process, VADecide achieves higher prediction accuracy compared to a traditional machine learning model. The study applies this approach to a parcel-level case study in Savannah, Georgia, revealing differences between machine-generated and human-generated results. This work contributes to the understanding of the benefits and limitations of human-in-the-loop machine learning (HITLML) in urban planning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VADecide is a new way to help cities find old buildings that are not being used. Usually, this is a hard problem because there’s no easy way to tell which properties are abandoned or falling apart. The researchers created VADecide by mixing human ideas with machine learning. They tested it in Savannah, Georgia and found it worked better than just using a computer model alone. This helps us understand how humans can help computers make better decisions about where to focus on fixing up old buildings. |
Keywords
* Artificial intelligence * Machine learning
