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Open AccessArticle
AI and Deep Learning for Image-Based Segmentation of Ancient Masonry: A Digital Methodology for Mensiochronology of Roman Brick
by
Lorenzo Fornaciari
Lorenzo Fornaciari
Lorenzo Fornaciari graduated in Classical Archaeology at La Sapienza University of Rome. Since 2012, [...]
Lorenzo Fornaciari graduated in Classical Archaeology at La Sapienza University of Rome. Since 2012, he has been collaborating on the ‘Curiae Veteres’ research project on the north-eastern slopes of the Palatine Hill as survey manager and GIS expert. Between 2017 and 2022, he participated in several research projects of landscape archaeology at the University of Salerno, where in 2022, he completed his PhD with a thesis on the chaîne opératoire of the construction site of the so-called ‘Baths of Elagabalus’ on the Palatine Hill. From 2015 to 2022, he participated as an archaeologist and topographer in various preventive archaeology excavations and projects for the valorisation of cultural heritage. Between 2018 and 2019, he was involved in the Great Pompeii Project. Since January 2022, he has held the position of technical surveyor at the École française de Rome. Since 2024, he has been an adjunct professor of ‘Digital Technologies for the Survey and Technical Analysis of Ancient Monuments’ (L-ANT/09) at the International Telematic University Uninettuno - Faculty of Cultural Heritage. His main research interests are in the fields of architectural and construction archaeology, topography and digital technologies applied to cultural heritage, with particular reference to archaeological and architectural survey methods and techniques, and the processing of geographical data in GIS for intra- and infra-site spatial analysis.
École Française de Rome, 00186 Roma, Italy
Heritage 2025, 8(7), 241; https://doi.org/10.3390/heritage8070241 (registering DOI)
Submission received: 1 April 2025
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Revised: 26 May 2025
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Accepted: 29 May 2025
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Published: 21 June 2025
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MDPI and ACS Style
Fornaciari, L.
AI and Deep Learning for Image-Based Segmentation of Ancient Masonry: A Digital Methodology for Mensiochronology of Roman Brick. Heritage 2025, 8, 241.
https://doi.org/10.3390/heritage8070241
AMA Style
Fornaciari L.
AI and Deep Learning for Image-Based Segmentation of Ancient Masonry: A Digital Methodology for Mensiochronology of Roman Brick. Heritage. 2025; 8(7):241.
https://doi.org/10.3390/heritage8070241
Chicago/Turabian Style
Fornaciari, Lorenzo.
2025. "AI and Deep Learning for Image-Based Segmentation of Ancient Masonry: A Digital Methodology for Mensiochronology of Roman Brick" Heritage 8, no. 7: 241.
https://doi.org/10.3390/heritage8070241
APA Style
Fornaciari, L.
(2025). AI and Deep Learning for Image-Based Segmentation of Ancient Masonry: A Digital Methodology for Mensiochronology of Roman Brick. Heritage, 8(7), 241.
https://doi.org/10.3390/heritage8070241
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