Summary of Window to Wall Ratio Detection Using Segformer, by Zoe De Simone et al.
Window to Wall Ratio Detection using SegFormer
by Zoe De Simone, Sayandeep Biswas, Oscar Wu
First submitted to arxiv on: 4 Jun 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 research paper presents a novel approach to predicting Window-to-Wall Ratios (WWR) of buildings from external street view images. By leveraging existing computer vision window detection methods and semantic segmentation techniques, the authors demonstrate the potential for adapting established computer vision technology in architectural applications. Specifically, the study utilizes semantic segmentation to predict WWRs based on image data, showcasing the promise of this approach in improving building performance simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer programs to look at pictures of buildings and figure out how much of the wall is taken up by windows. This helps people design better buildings that use energy efficiently, get enough sunlight, and stay cool. Normally, people just assume all buildings have 40% of their walls taken up by windows, but this method can be more accurate. |
Keywords
» Artificial intelligence » Semantic segmentation
