Synthetic Training Datasets for Architectural Conservation: A Deep Learning Approach for Decay Detection
Architectural heritage conservation increasingly relies on innovative tools for detecting and monitoring degradation. The study presented in the current paper explores the use of synthetic datasets—namely, rendered images derived from photogrammetric models—to train convolutional neural networks (CN...
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/10/1714 |
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| author | Giacomo Patrucco Francesco Setragno Antonia Spanò |
| author_facet | Giacomo Patrucco Francesco Setragno Antonia Spanò |
| author_sort | Giacomo Patrucco |
| collection | DOAJ |
| description | Architectural heritage conservation increasingly relies on innovative tools for detecting and monitoring degradation. The study presented in the current paper explores the use of synthetic datasets—namely, rendered images derived from photogrammetric models—to train convolutional neural networks (CNNs) for the automated detection of deterioration in historical reinforced concrete structures. The primary objective is to assess the effectiveness of synthetic images for deep learning training, comparing their performance with models trained on traditional datasets. The research focuses on a significant case study: the parabolic concrete arch of Morano sul Po. Two classification scenarios were tested: a single-class model for structure recognition and a multi-class model for identifying degradation patterns, such as exposed reinforcement bars. The findings indicate that synthetic datasets can effectively support structure identification, achieving results comparable to those obtained with real-world imagery. However, challenges arise in multi-class classification, particularly in distinguishing fine-grained degradation features. This study highlights the potential of artificial datasets in overcoming the limitations of annotated data availability in heritage conservation. The proposed approach represents a promising step toward automating documentation and damage assessment, ultimately contributing to more efficient and scalable heritage monitoring strategies. |
| format | Article |
| id | doaj-art-7f7b7ad586e04ed2a57782fd42992384 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-7f7b7ad586e04ed2a57782fd429923842025-08-20T01:56:45ZengMDPI AGRemote Sensing2072-42922025-05-011710171410.3390/rs17101714Synthetic Training Datasets for Architectural Conservation: A Deep Learning Approach for Decay DetectionGiacomo Patrucco0Francesco Setragno1Antonia Spanò2Lab G4CH, Department of Architecture and Design (DAD), Politecnico di Torino, Viale Mattioli, 39, 10125 Torino, ItalyVolta Robots srl, Via Roberto Lepetit, 34, 21040 Gerenzano, ItalyLab G4CH, Department of Architecture and Design (DAD), Politecnico di Torino, Viale Mattioli, 39, 10125 Torino, ItalyArchitectural heritage conservation increasingly relies on innovative tools for detecting and monitoring degradation. The study presented in the current paper explores the use of synthetic datasets—namely, rendered images derived from photogrammetric models—to train convolutional neural networks (CNNs) for the automated detection of deterioration in historical reinforced concrete structures. The primary objective is to assess the effectiveness of synthetic images for deep learning training, comparing their performance with models trained on traditional datasets. The research focuses on a significant case study: the parabolic concrete arch of Morano sul Po. Two classification scenarios were tested: a single-class model for structure recognition and a multi-class model for identifying degradation patterns, such as exposed reinforcement bars. The findings indicate that synthetic datasets can effectively support structure identification, achieving results comparable to those obtained with real-world imagery. However, challenges arise in multi-class classification, particularly in distinguishing fine-grained degradation features. This study highlights the potential of artificial datasets in overcoming the limitations of annotated data availability in heritage conservation. The proposed approach represents a promising step toward automating documentation and damage assessment, ultimately contributing to more efficient and scalable heritage monitoring strategies.https://www.mdpi.com/2072-4292/17/10/1714deep learningartificial training datasets generationautomatic classificationdecay detectionconcrete heritage |
| spellingShingle | Giacomo Patrucco Francesco Setragno Antonia Spanò Synthetic Training Datasets for Architectural Conservation: A Deep Learning Approach for Decay Detection Remote Sensing deep learning artificial training datasets generation automatic classification decay detection concrete heritage |
| title | Synthetic Training Datasets for Architectural Conservation: A Deep Learning Approach for Decay Detection |
| title_full | Synthetic Training Datasets for Architectural Conservation: A Deep Learning Approach for Decay Detection |
| title_fullStr | Synthetic Training Datasets for Architectural Conservation: A Deep Learning Approach for Decay Detection |
| title_full_unstemmed | Synthetic Training Datasets for Architectural Conservation: A Deep Learning Approach for Decay Detection |
| title_short | Synthetic Training Datasets for Architectural Conservation: A Deep Learning Approach for Decay Detection |
| title_sort | synthetic training datasets for architectural conservation a deep learning approach for decay detection |
| topic | deep learning artificial training datasets generation automatic classification decay detection concrete heritage |
| url | https://www.mdpi.com/2072-4292/17/10/1714 |
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