# Window to Wall Ratio Detection using SegFormer

arXiv: https://arxiv.org/abs/2406.02706 · HTML: https://mlsanity.com/paper/2406.02706/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [End-to-End Instance Edge Detection](https://mlsanity.com/paper/2204.02898/) (2022)

## Built on by (most cited first, 1 of 1)

- [Exploiting Semantic Scene Reconstruction for Estimating Building Envelope Characteristics](https://mlsanity.com/paper/2410.22383/) (2024)

## Abstract (arXiv)

Window to Wall Ratios (WWR) are key to assessing the energy, daylight and ventilation performance of buildings. Studies have shown that window area has a large impact on building performance and simulation. However, data to set up these environmental models and simulations is typically not available. Instead, a standard 40% WWR is typically assumed for all buildings. This paper leverages existing computer vision window detection methods to predict WWR of buildings from external street view images using semantic segmentation, demonstrating the potential for adapting established computer vision technique in architectural applications