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構造物の外観の背後をAIで可視化(Researchers use AI to ‘see’ beyond a structure’s facade in Google Street View)

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2025-05-22 トロント大学

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U of T researchers Shoshanna Saxe, left, and Alex Olson say their approach could help urban planners better understand cities’ resource needs and prioritize future infrastructure projects (photo by Phill Snel)

トロント大学の研究チームは、Googleストリートビューの画像と人工知能(AI)を活用し、建物の外観から内部の情報(築年数や床面積など)を推定する手法を開発しました。この技術は、建物の資材フローや温室効果ガス排出量の評価に役立ち、都市計画やインフラ整備の効率化に貢献します。AIモデルは、建物の築年数を約70%の精度で、床面積を約80%の精度で予測可能です。この手法は、従来数百万ドルかかっていたデータ収集を約1,000ドルで実現し、都市全体の建物データを迅速かつ低コストで取得する新たな手段として注目されています。

<関連情報>

ディープラーニングによる画像ベースの住宅建物属性予測 Image-based prediction of residential building attributes with deep learning

Weimin Huang, Alexander W. Olson, Elias B. Khalil, Shoshanna Saxe
Journal of Industrial Ecology  Published: 19 November 2024
DOI:https://doi.org/10.1111/jiec.13591

構造物の外観の背後をAIで可視化(Researchers use AI to ‘see’ beyond a structure’s facade in Google Street View)

Abstract

This study estimates building attributes—floor area and age—using image-based machine learning. Building age and floor area are key inputs to the studies of urban metabolism, material stocks and flows, and embodied greenhouse gases (GHGs) in the built environment. However, these data are challenging to generate and maintain using traditional survey methods, their availability is uneven and often, even when available, very uncertain. Improving our understanding and future management of built environment resource flows and associated environmental impacts requires more complete access to building age and floor area data. The study formulates area prediction as a regression problem and age prediction as a classification problem over six historical periods, achieving a mean absolute percentage error of 19.42% for area prediction and an accuracy of 70.27% for age prediction in Toronto. These results are obtained using an EfficientNetV2 module for feature extraction from Google Street View images, followed by fully connected layers for estimating the two building attributes. The performance of the Toronto-trained model in five other Canadian cities is also reported, highlighting the model’s varying effectiveness in different urban contexts and the benefit of local training. Our findings demonstrate the feasibility of using machine learning for building attribute estimation from street-view images, offering a basis for future automated large-scale material flow and stock analysis.

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