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<p>Machine learning techniques can fill data gaps for urban-scale building simulations, particularly gaps around window-to-wall ratio (WWR). This study presents a comprehensive workflow to (1) automatically extract and stitch images from Google Street View (GSV); (2) label images with a custom Rhino-based tool to aid annotation of occluded glazing; (3) detect wall, garage, and glazing objects by training and validating a YOLOv9 deep learning model with three added post-scripts; (4) calculate WWR at façade, building, and district scales; and (5) simulate district energy consumption in an urban building energy model (UBEM). Results include a 96% image-capture rate from GSV, indicating a robust extraction and stitching algorithm. Converting model detections into WWR, 94% and 100% of façades have detected WWRs within ±5% and ±10% of ground truth WWRs, respectively. A novel automatic algorithm upscales façade detection to estimate WWR at non-street-facing sides and rears, resulting in distinct WWRs for each face of each building. For a case study in Turin, Italy, WWR detections are +5.2% and +6.9% greater when upscaling based on OpenStreetMap and municipal GIS data, respectively, compared to TABULA, leading to 1.5% and 35.5% increases in heating and cooling energy need in the UBEM. The workflow is made openly available to support future research in other contexts.</p>

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Detecting window-to-wall ratio for urban-scale building simulations using deep learning with street view imagery and an automatic classification algorithm

  • 期刊论文

  • 发表日期：

    2025年6月28日

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  • Anthony Robert Suppa,
  • Alessandro Aliberti,
  • Marta Carla Bottero,
  • Vincenzo Corrado

摘要

Machine learning techniques can fill data gaps for urban-scale building simulations, particularly gaps around window-to-wall ratio (WWR). This study presents a comprehensive workflow to (1) automatically extract and stitch images from Google Street View (GSV); (2) label images with a custom Rhino-based tool to aid annotation of occluded glazing; (3) detect wall, garage, and glazing objects by training and validating a YOLOv9 deep learning model with three added post-scripts; (4) calculate WWR at façade, building, and district scales; and (5) simulate district energy consumption in an urban building energy model (UBEM). Results include a 96% image-capture rate from GSV, indicating a robust extraction and stitching algorithm. Converting model detections into WWR, 94% and 100% of façades have detected WWRs within ±5% and ±10% of ground truth WWRs, respectively. A novel automatic algorithm upscales façade detection to estimate WWR at non-street-facing sides and rears, resulting in distinct WWRs for each face of each building. For a case study in Turin, Italy, WWR detections are +5.2% and +6.9% greater when upscaling based on OpenStreetMap and municipal GIS data, respectively, compared to TABULA, leading to 1.5% and 35.5% increases in heating and cooling energy need in the UBEM. The workflow is made openly available to support future research in other contexts.