Decoding building facades : automated element and material recognition in street-level images for large-scale building stock assessments

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Noll, Leonhard and Noichl, Florian and Kiper, Beyza and Borrmann, André; Moreno-Rangel, Alejandro and Kumar, Bimal, eds. (2025) Decoding building facades : automated element and material recognition in street-level images for large-scale building stock assessments. In: EG-ICE 2025. University of Strathclyde Publishing, GBR, pp. 558-568. ISBN 9781914241826 (https://doi.org/10.17868/strath.00093240)

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Abstract

As a basis for environmental assessments like Life Cycle Assessment (LCA) of large building portfolios, extensive image data of building envelopes must be evaluated as automatically as possible. This paper addresses the automated detection of both elements – e.g., windows, walls, doors – and materials in building facades with unified machine learning workflows using 2D RGB images. Following a systematic review of existing methods and datasets, two unified segmentation workflows are developed: Hierarchical Segmentation (HS) and Multi-Task Learning (MTL). HS exploits the hierarchical relationships between facade elements and materials and deploys a post-prediction clustering approach with Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), while MTL leverages shared feature learning for simultaneous detection. To mitigate limited training data, this work introduces the high-resolution segmentation and classification dataset Facades Material Munich (FaMatMuc). For the first time, element and material detection for facade images were combined in one workflow and validated successfully.

  • Item type:Book Section
    ID code:93240
    Dates:
    Date
    Event
    1 July 2025
    Published
    9 June 2025
    Accepted
    Subjects:Fine Arts > Architecture
    Department:Faculty of Engineering > Architecture
    Depositing user: Pure Administrator
    Date deposited:27 Jun 2025 09:47
    Last modified:01 Apr 2026 15:23
    URI:https://strathprints.strath.ac.uk/id/eprint/93240
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