AI and Deep Learning for Image-Based Segmentation of Ancient Masonry: A Digital Methodology for Mensiochronology of Roman Brick
In the field of building archaeology, the analysis of wall surfaces represents a fundamental tool for the study of an architecture and its construction phases. In fact, masonry stores valuable information regarding not only used materials and construction techniques but also transformations happen o...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2571-9408/8/7/241 |
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| author | Lorenzo Fornaciari |
| author_facet | Lorenzo Fornaciari |
| author_sort | Lorenzo Fornaciari |
| collection | DOAJ |
| description | In the field of building archaeology, the analysis of wall surfaces represents a fundamental tool for the study of an architecture and its construction phases. In fact, masonry stores valuable information regarding not only used materials and construction techniques but also transformations happen over time for natural events or anthropic interventions. The traditional approach to the analysis of building materials is mainly based on direct observation and manual annotations based on orthophotos obtained through photogrammetric surveys. This process, while providing a high degree of accuracy and understanding, is extremely time- and resource-consuming. In addition, the lack of standardised procedures for the statistical analysis of measurements leads to data that are difficult to compare for different contexts. Time and subjectivity are ultimately the two main limitations that most hinder the diffusion of the mensiochronological approach and for this reason, the most recent artificial intelligence solutions for the segmentation and extraction of measurements of individual masonry components will be addressed. Finally, a workflow will be presented based on image segmentation using machine learning models and the automatic extraction and statistical analysis of measurements using a script designed specifically by the author for the mensiochronological analysis of Roman brick masonry. |
| format | Article |
| id | doaj-art-d37f37a809b14502bc130fc76a39e5d2 |
| institution | Matheson Library |
| issn | 2571-9408 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Heritage |
| spelling | doaj-art-d37f37a809b14502bc130fc76a39e5d22025-07-25T13:24:11ZengMDPI AGHeritage2571-94082025-06-018724110.3390/heritage8070241AI and Deep Learning for Image-Based Segmentation of Ancient Masonry: A Digital Methodology for Mensiochronology of Roman BrickLorenzo Fornaciari0École Française de Rome, 00186 Roma, ItalyIn the field of building archaeology, the analysis of wall surfaces represents a fundamental tool for the study of an architecture and its construction phases. In fact, masonry stores valuable information regarding not only used materials and construction techniques but also transformations happen over time for natural events or anthropic interventions. The traditional approach to the analysis of building materials is mainly based on direct observation and manual annotations based on orthophotos obtained through photogrammetric surveys. This process, while providing a high degree of accuracy and understanding, is extremely time- and resource-consuming. In addition, the lack of standardised procedures for the statistical analysis of measurements leads to data that are difficult to compare for different contexts. Time and subjectivity are ultimately the two main limitations that most hinder the diffusion of the mensiochronological approach and for this reason, the most recent artificial intelligence solutions for the segmentation and extraction of measurements of individual masonry components will be addressed. Finally, a workflow will be presented based on image segmentation using machine learning models and the automatic extraction and statistical analysis of measurements using a script designed specifically by the author for the mensiochronological analysis of Roman brick masonry.https://www.mdpi.com/2571-9408/8/7/241AIarchaeologyarchitectureRomanbrick |
| spellingShingle | Lorenzo Fornaciari AI and Deep Learning for Image-Based Segmentation of Ancient Masonry: A Digital Methodology for Mensiochronology of Roman Brick Heritage AI archaeology architecture Roman brick |
| title | AI and Deep Learning for Image-Based Segmentation of Ancient Masonry: A Digital Methodology for Mensiochronology of Roman Brick |
| title_full | AI and Deep Learning for Image-Based Segmentation of Ancient Masonry: A Digital Methodology for Mensiochronology of Roman Brick |
| title_fullStr | AI and Deep Learning for Image-Based Segmentation of Ancient Masonry: A Digital Methodology for Mensiochronology of Roman Brick |
| title_full_unstemmed | AI and Deep Learning for Image-Based Segmentation of Ancient Masonry: A Digital Methodology for Mensiochronology of Roman Brick |
| title_short | AI and Deep Learning for Image-Based Segmentation of Ancient Masonry: A Digital Methodology for Mensiochronology of Roman Brick |
| title_sort | ai and deep learning for image based segmentation of ancient masonry a digital methodology for mensiochronology of roman brick |
| topic | AI archaeology architecture Roman brick |
| url | https://www.mdpi.com/2571-9408/8/7/241 |
| work_keys_str_mv | AT lorenzofornaciari aianddeeplearningforimagebasedsegmentationofancientmasonryadigitalmethodologyformensiochronologyofromanbrick |