Machine Learning-Based Detection of Stone Degradation using TLS, Photographs and HBIM: A Case Study on the Lausanne Cathedral
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Date
2025
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Historic sandstone structures are vulnerable to environmental degradation, requiring efficient damage assessment and documentation for effective conservation. Traditional manual mapping methods are time-intensive, cost-intensive, and challenging to standardize. Herein we present a semi-automated workflow for damage classification and integration into a historic building information model (HBIM) using terrestrial laser scanning (TLS), orthophotos, and machine learning. A Random Forest model, optimised through forward feature selection and Bayesian hyperparameter tuning, classifies degradation types (contour scaling, biodeterioration, black crust formation and exfoliation) based on geometric and radiometric features. Finally, degradation information is linked to individual stone blocks in the HBIM, and degradation maps can be generated. In a case study on the Lausanne Cathedral, we achieve an overall classification accuracy of 80% using this approach. The results highlight the potential of using machine-learning techniques with TLS data for an efficient and scalable heritage condition assessment.
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published
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Book title
STONE 2025 - Proceedings of the 15th International Congress on the Deterioration and Conservation of Stone, Volume 1 & 2
Journal / series
Volume
Pages / Article No.
565 - 571
Publisher
15th International Congress on the Deterioration and Conservation of Stone
Event
15th International Congress on the Deterioration and Conservation of Stone (STONE 2025)
Edition / version
Methods
Software
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Date collected
Date created
Subject
HBIM; LiDAR; point clouds; degradation mapping; machine learning; cultural heritage
Organisational unit
03964 - Wieser, Andreas / Wieser, Andreas
03891 - Flatt, Robert J. / Flatt, Robert J.
Notes
Conference lecture held on September 10, 2025