# Window to Wall Ratio Detection using SegFormer

Source: [Window to Wall Ratio Detection using SegFormer](<https://www.sota2.com/research/paper/68fa73711e209131cea5c88a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:50:15.439Z

Authors: Zoe De Simone, Sayandeep Biswas, Oscar Wu

Published: 2024-06-04T18:36:11.000Z

## Abstract

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

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.02706>)

[PDF](<https://arxiv.org/pdf/2406.02706.pdf>)