Pub Date : 2025-12-01Epub Date: 2025-10-01DOI: 10.1016/j.daach.2025.e00467
Miguel Ángel Maté-González , Cristina Sáez Blázquez , Jesús Rodríguez-Hernández , Jesús R. Álvarez-Sanchís , Serafín López-Cuervo Medina
The VETTONIA project is focused on evaluating the rich Iron Age heritage of the western Iberian Peninsula and highlighting the recent archaeological research in the area with advanced technologies: virtual tours, 3D models, and immersive simulations. The final project presents dynamic, interactive formats, sparking interest across diverse audiences in different lectures, seminars, and forums. Its initiatives are presented during the annual archaeological interventions at the oppidum of Ulaca (Solosancho, Ávila, Spain), with excellent response by the attending public. A survey was conducted among participants to validate the results, yielding highly positive feedback on the project's impact and accessibility. The project stands as an innovative model in heritage dissemination, demonstrating the educational potential of new technologies. In the future, such resources could become essential for classroom education at several levels and encourage sustainable tourism in delicate natural environments like the major Late Iron Age settlements (approximately 400–50 BC).
{"title":"Unveiling history: An innovative approach to the dissemination of archaeological heritage","authors":"Miguel Ángel Maté-González , Cristina Sáez Blázquez , Jesús Rodríguez-Hernández , Jesús R. Álvarez-Sanchís , Serafín López-Cuervo Medina","doi":"10.1016/j.daach.2025.e00467","DOIUrl":"10.1016/j.daach.2025.e00467","url":null,"abstract":"<div><div>The VETTONIA project is focused on evaluating the rich Iron Age heritage of the western Iberian Peninsula and highlighting the recent archaeological research in the area with advanced technologies: virtual tours, 3D models, and immersive simulations. The final project presents dynamic, interactive formats, sparking interest across diverse audiences in different lectures, seminars, and forums. Its initiatives are presented during the annual archaeological interventions at the oppidum of Ulaca (Solosancho, Ávila, Spain), with excellent response by the attending public. A survey was conducted among participants to validate the results, yielding highly positive feedback on the project's impact and accessibility. The project stands as an innovative model in heritage dissemination, demonstrating the educational potential of new technologies. In the future, such resources could become essential for classroom education at several levels and encourage sustainable tourism in delicate natural environments like the major Late Iron Age settlements (approximately 400–50 BC).</div></div>","PeriodicalId":38225,"journal":{"name":"Digital Applications in Archaeology and Cultural Heritage","volume":"39 ","pages":"Article e00467"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-09-16DOI: 10.1016/j.daach.2025.e00461
Rafael Fernandes Dionizio, Eloisa Dezen-Kempter
Managing heritage assets at an urban scale is a complex task due to the unique characteristics of the buildings and their large scale. Technologies like Historic Building Information Modeling and Historical GIS, combined with digital scanning and 2D data, have emerged to address these challenges. While HBIM and HGIS integration facilitates the extraction and transformation of geometric and semantic information, issues such as data loss during software transfers and the need to adhere to specific standards pose significant challenges. This study focuses on the integration of HBIM and HGIS, using the Pampulha Art Museum as recognized by UNESCO, as a study object. A qualitative exploratory methodology is employed to develop the HBIM-HGIS model and present it via a web-based GIS viewer. The study explores various data integration methods, their advantages, and limitations, contributing to a deeper understanding of HBIM-HGIS integration and offering a foundation for advancing architectural heritage management.
{"title":"HBIM-HGIS for a multi-level knowledge-based approach","authors":"Rafael Fernandes Dionizio, Eloisa Dezen-Kempter","doi":"10.1016/j.daach.2025.e00461","DOIUrl":"10.1016/j.daach.2025.e00461","url":null,"abstract":"<div><div>Managing heritage assets at an urban scale is a complex task due to the unique characteristics of the buildings and their large scale. Technologies like Historic Building Information Modeling and Historical GIS, combined with digital scanning and 2D data, have emerged to address these challenges. While HBIM and HGIS integration facilitates the extraction and transformation of geometric and semantic information, issues such as data loss during software transfers and the need to adhere to specific standards pose significant challenges. This study focuses on the integration of HBIM and HGIS, using the Pampulha Art Museum as recognized by UNESCO, as a study object. A qualitative exploratory methodology is employed to develop the HBIM-HGIS model and present it via a web-based GIS viewer. The study explores various data integration methods, their advantages, and limitations, contributing to a deeper understanding of HBIM-HGIS integration and offering a foundation for advancing architectural heritage management.</div></div>","PeriodicalId":38225,"journal":{"name":"Digital Applications in Archaeology and Cultural Heritage","volume":"39 ","pages":"Article e00461"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-30DOI: 10.1016/j.daach.2025.e00473
Weinan Zhao , Alexander Hohman
This study explores how Virtual Reality (VR) can reconstruct Antonio Sant’Elia's unbuilt Futurist city, Città Nuova, transforming visionary architectural speculation into a tangible cultural experience. By combining digital modeling with iterative user feedback, the research demonstrates how immersive technologies can recover lost architectural intentions while revealing their experiential and spatial logic. The project culminates in a fully navigable VR environment developed in Unreal Engine, illustrating both the possibilities and constraints of applying VR in architectural heritage reconstruction. The findings show that VR functions not only as a visualization tool but also as an interpretive medium, translating speculative design into embodied, experiential knowledge. This workflow provides a replicable framework for studying visionary architecture through digital experimentation.
本研究探讨了虚拟现实（VR）如何重建安东尼奥·圣埃利亚（Antonio Sant 'Elia）未建成的未来主义城市citt Nuova，将有远见的建筑投机转化为有形的文化体验。通过将数字建模与迭代用户反馈相结合，该研究展示了沉浸式技术如何在揭示其体验和空间逻辑的同时恢复丢失的建筑意图。该项目最终在虚幻引擎中开发了一个完全可导航的VR环境，说明了在建筑遗产重建中应用VR的可能性和限制。研究结果表明，VR不仅可以作为一种可视化工具，还可以作为一种解释媒介，将推测性设计转化为具体的体验性知识。这个工作流程为通过数字实验研究有远见的建筑提供了一个可复制的框架。
{"title":"Resurrecting Antonio Sant'Elia's futurist vision: A journey through digital modeling and virtual reality","authors":"Weinan Zhao , Alexander Hohman","doi":"10.1016/j.daach.2025.e00473","DOIUrl":"10.1016/j.daach.2025.e00473","url":null,"abstract":"<div><div>This study explores how Virtual Reality (VR) can reconstruct Antonio Sant’Elia's unbuilt Futurist city, Città Nuova, transforming visionary architectural speculation into a tangible cultural experience. By combining digital modeling with iterative user feedback, the research demonstrates how immersive technologies can recover lost architectural intentions while revealing their experiential and spatial logic. The project culminates in a fully navigable VR environment developed in Unreal Engine, illustrating both the possibilities and constraints of applying VR in architectural heritage reconstruction. The findings show that VR functions not only as a visualization tool but also as an interpretive medium, translating speculative design into embodied, experiential knowledge. This workflow provides a replicable framework for studying visionary architecture through digital experimentation.</div></div>","PeriodicalId":38225,"journal":{"name":"Digital Applications in Archaeology and Cultural Heritage","volume":"39 ","pages":"Article e00473"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-09-23DOI: 10.1016/j.daach.2025.e00458
Zied Mnasri , Andrea D’Andrea
The inventory of a large collection of archaeological artifacts can be a tedious and time-consuming task. However, nowadays it is possible to reduce its complexity through the use of artificial intelligence tools, including object detection and classification. Deep learning is particularly an AI method which is highly effective for information retrieval and exploration of big datasets. In this work, a technique based on deep learning is applied on an archaeological dataset documenting the discoveries made by the Italian archaeological team at the Al-Baleed site in Oman. The suggested method seeks to: (a) segment photos into individual artifacts; (b) define the segmented artifacts (e.g., pottery, vessel pieces, jewellery, etc.); and (c) categorize the recognized items based on the material used in their handcraft (e.g., earthenware, glass, metal alloy, etc.). Two different kinds of deep neural network models were used to accomplish this twofold function. The first one was used for object identification and was based on Google’s TensorFlow2 Object Detection API, while the second one was created from scratch and trained to categorize the materials of an artifact. An on-site photo collection served as the dataset for training, validating, and testing both varieties of neural nets. However, data augmentation was carried out to provide more training sample versions in order to improve the models’ generalization ability. Evaluation was achieved using standard metrics for each task, such as the mean Average Precision (mAP) for object identification and the overall Accuracy for classification. The findings indicate a good rate of object detection and identification and, more importantly, a satisfactory accuracy of the artifact material’s classification. Besides, benchmarking with state-of-the-art image classification methods, based on transfer learning models, namely SqueezeNet and GoogleNet, which are trained on bigger datasets such as ImageNet, show that the accuracy of the proposed approach attain a comparable accuracy, with the advantage to be specifically trained on the studied dataset. As a result, the models could potentially be used to firstly for creating an automatic inventory process for the archaeological artifacts, and secondly uncover patterns in archaeological data that are currently unknown to assist the identification of items within a sizeable dataset.
{"title":"Automatic inventory of archaeological artifacts based on object detection and classification using deep and transfer learning","authors":"Zied Mnasri , Andrea D’Andrea","doi":"10.1016/j.daach.2025.e00458","DOIUrl":"10.1016/j.daach.2025.e00458","url":null,"abstract":"<div><div>The inventory of a large collection of archaeological artifacts can be a tedious and time-consuming task. However, nowadays it is possible to reduce its complexity through the use of artificial intelligence tools, including object detection and classification. Deep learning is particularly an AI method which is highly effective for information retrieval and exploration of big datasets. In this work, a technique based on deep learning is applied on an archaeological dataset documenting the discoveries made by the Italian archaeological team at the Al-Baleed site in Oman. The suggested method seeks to: (a) segment photos into individual artifacts; (b) define the segmented artifacts (e.g., pottery, vessel pieces, jewellery, etc.); and (c) categorize the recognized items based on the material used in their handcraft (e.g., earthenware, glass, metal alloy, etc.). Two different kinds of deep neural network models were used to accomplish this twofold function. The first one was used for object identification and was based on Google’s TensorFlow2 Object Detection API, while the second one was created from scratch and trained to categorize the materials of an artifact. An on-site photo collection served as the dataset for training, validating, and testing both varieties of neural nets. However, data augmentation was carried out to provide more training sample versions in order to improve the models’ generalization ability. Evaluation was achieved using standard metrics for each task, such as the mean Average Precision (mAP) for object identification and the overall Accuracy for classification. The findings indicate a good rate of object detection and identification and, more importantly, a satisfactory accuracy of the artifact material’s classification. Besides, benchmarking with state-of-the-art image classification methods, based on transfer learning models, namely SqueezeNet and GoogleNet, which are trained on bigger datasets such as ImageNet, show that the accuracy of the proposed approach attain a comparable accuracy, with the advantage to be specifically trained on the studied dataset. As a result, the models could potentially be used to firstly for creating an automatic inventory process for the archaeological artifacts, and secondly uncover patterns in archaeological data that are currently unknown to assist the identification of items within a sizeable dataset.</div></div>","PeriodicalId":38225,"journal":{"name":"Digital Applications in Archaeology and Cultural Heritage","volume":"39 ","pages":"Article e00458"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-03DOI: 10.1016/j.daach.2025.e00464
Gabriele Ciccone , Alessia Frisetti , Nicodemo Abate , Roberto Goffredo , Giorgia Dato , Antonio Minervino Amodio , Maria Sileo , Rosa Lasaponara , Nicola Masini
{"title":"Unveiling medieval landscapes: A virtual reconstruction through LiDAR and extended matrix methodology, the case of Torre di Castiglione (BA)","authors":"Gabriele Ciccone , Alessia Frisetti , Nicodemo Abate , Roberto Goffredo , Giorgia Dato , Antonio Minervino Amodio , Maria Sileo , Rosa Lasaponara , Nicola Masini","doi":"10.1016/j.daach.2025.e00464","DOIUrl":"10.1016/j.daach.2025.e00464","url":null,"abstract":"","PeriodicalId":38225,"journal":{"name":"Digital Applications in Archaeology and Cultural Heritage","volume":"39 ","pages":"Article e00464"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores the application of advanced deep learning techniques in analyzing Upper Palaeolithic hand stencil representations, focusing on sex classification of individuals involved in prehistoric rock art activity. The research highlights the effectiveness of deep learning models, particularly EfficientNetV2-S, which achieved an accuracy rate of 81.03 % for experimental blown hand stencils and 95.08 % in delineated contemporary hand image samples for sex identification, surpassing traditional morphometric methods. The study demonstrates that deep learning can differentiate male and female hand stencils with high precision, suggesting a mixed-sexual participation in creating prehistoric art, with a slight prevalence of male hand representations in the studied caves. The integration of user-friendly platforms, such as Google Colab, facilitates the reproducibility and validation of these findings, promoting methodological transparency. However, the accuracy of deep learning models is contingent on the quality and preservation of the images, presenting challenges when working with deteriorated or incomplete samples. This work highlights the potential of advanced technologies in archaeological research, opening new avenues for investigating the creation of prehistoric graphic expressions and their social implications.
{"title":"Recognizing past shapes: sex differentiation through deep learning on European Upper Palaeolithic hand stencils","authors":"Verónica Fernández-Navarro , Aitor González-Marfil , Ignacio Arganda-Carreras , Diego Garate","doi":"10.1016/j.daach.2025.e00453","DOIUrl":"10.1016/j.daach.2025.e00453","url":null,"abstract":"<div><div>This study explores the application of advanced deep learning techniques in analyzing Upper Palaeolithic hand stencil representations, focusing on sex classification of individuals involved in prehistoric rock art activity. The research highlights the effectiveness of deep learning models, particularly EfficientNetV2-S, which achieved an accuracy rate of 81.03 % for experimental blown hand stencils and 95.08 % in delineated contemporary hand image samples for sex identification, surpassing traditional morphometric methods. The study demonstrates that deep learning can differentiate male and female hand stencils with high precision, suggesting a mixed-sexual participation in creating prehistoric art, with a slight prevalence of male hand representations in the studied caves. The integration of user-friendly platforms, such as Google Colab, facilitates the reproducibility and validation of these findings, promoting methodological transparency. However, the accuracy of deep learning models is contingent on the quality and preservation of the images, presenting challenges when working with deteriorated or incomplete samples. This work highlights the potential of advanced technologies in archaeological research, opening new avenues for investigating the creation of prehistoric graphic expressions and their social implications.</div></div>","PeriodicalId":38225,"journal":{"name":"Digital Applications in Archaeology and Cultural Heritage","volume":"38 ","pages":"Article e00453"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-06-17DOI: 10.1016/j.daach.2025.e00441
Giulia Marsili , Stephan N. Hassam
Legacy data from archaeological sites with long excavation histories present both challenges and opportunities for modern research. Such data - ranging from handwritten notes, excavation diaries, and photographs to artefacts and related inventories - often predate contemporary recording standards, yet they can hold invaluable information about archaeological sites that did not make it into the publication record. This research situates itself within the broader theoretical framework of “archive archaeology,” in the context of a digitization project at the Villa del Casale. Using existing AI technologies such as Transkribus and the Handwriting Analysis Tool, the project approaches archives not only as repositories of information but also as subjects of study. It explores the potential of these advanced digital technologies to transcribe and interpret unpublished handwritten legacy data - specifically, archival materials related to earlier excavations at the Villa del Casale in Piazza Armerina - in order to contribute to the broader analysis and understanding of legacy data and handwritten field notes. Additionally, the project discusses the creation of a Villa del Casale's digital ecosystem to enhance the dissemination, accessibility, and reuse of both primary and secondary research data through the use of the open-source web publishing platform Omeka Classic, designed for the creation and management of digital collections and exhibits. The approach taken in this research seeks to integrate different up-to-date digital technologies to bridge the gap between historical archives, archaeological legacy data, and contemporary archaeological inquiry.
具有悠久挖掘历史的考古遗址的遗产数据为现代研究带来了挑战和机遇。这些数据——从手写笔记、挖掘日记、照片到人工制品和相关清单——往往早于当代的记录标准，但它们可以保存有关考古遗址的宝贵信息，而这些信息没有进入出版记录。在Casale别墅的数字化项目背景下，这项研究将自己置于“档案考古学”的更广泛的理论框架中。利用现有的人工智能技术，如Transkribus和笔迹分析工具，该项目不仅将档案作为信息库，还将其作为研究对象。它探索了这些先进的数字技术转录和解释未发表的手写遗产数据的潜力，特别是与早期在阿梅里纳广场的Casale别墅挖掘相关的档案材料，以便对遗产数据和手写现场笔记进行更广泛的分析和理解。此外，该项目还讨论了Villa del Casale数字生态系统的创建，通过使用开源网络发布平台Omeka Classic来增强主要和次要研究数据的传播、可访问性和重用性，该平台专为数字收藏和展览的创建和管理而设计。本研究采用的方法旨在整合不同的最新数字技术，以弥合历史档案，考古遗产数据和当代考古调查之间的差距。
{"title":"From archives to museum and back: transcribing, digitizing, and enriching cultural heritage and manuscript legacy data of the Villa del Casale of Piazza Armerina","authors":"Giulia Marsili , Stephan N. Hassam","doi":"10.1016/j.daach.2025.e00441","DOIUrl":"10.1016/j.daach.2025.e00441","url":null,"abstract":"<div><div>Legacy data from archaeological sites with long excavation histories present both challenges and opportunities for modern research. Such data - ranging from handwritten notes, excavation diaries, and photographs to artefacts and related inventories - often predate contemporary recording standards, yet they can hold invaluable information about archaeological sites that did not make it into the publication record. This research situates itself within the broader theoretical framework of “archive archaeology,” in the context of a digitization project at the Villa del Casale. Using existing AI technologies such as Transkribus and the Handwriting Analysis Tool, the project approaches archives not only as repositories of information but also as subjects of study. It explores the potential of these advanced digital technologies to transcribe and interpret unpublished handwritten legacy data - specifically, archival materials related to earlier excavations at the Villa del Casale in Piazza Armerina - in order to contribute to the broader analysis and understanding of legacy data and handwritten field notes. Additionally, the project discusses the creation of a Villa del Casale's digital ecosystem to enhance the dissemination, accessibility, and reuse of both primary and secondary research data through the use of the open-source web publishing platform Omeka Classic, designed for the creation and management of digital collections and exhibits. The approach taken in this research seeks to integrate different up-to-date digital technologies to bridge the gap between historical archives, archaeological legacy data, and contemporary archaeological inquiry.</div></div>","PeriodicalId":38225,"journal":{"name":"Digital Applications in Archaeology and Cultural Heritage","volume":"38 ","pages":"Article e00441"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-08-06DOI: 10.1016/j.daach.2025.e00448
Le Cong Thuong, Viet Nam Le, Thanh Ha Le, Thi Duyen Ngo
The woodblock printing technique, first developed in China, enabled the large-scale production of texts and significantly advanced the spread of knowledge and literacy across many Asian countries for centuries. In Vietnam, the royal woodblocks of the Nguyen Dynasty are considered a national treasure. However, many of these woodblocks have been lost or damaged over time, making it imperative to develop a method for reconstructing them. Therefore, this paper proposes a data processing workflow capable of constructing a Sino-Nom character dataset from existing woodblock collections. The constructed dataset can then be used for reconstructing the lost woodblocks and for further in-depth analysis. Using the 3D collection of the Dai Nam Thuc Luc chronicle as an example, we have created a large Sino-Nom character dataset named SiNoC through our proposed workflow. The SiNoC dataset comprises 90,259 pairs of 3D and 2D Sino-Nom characters. This dataset serves as a foundation for deep learning models and advanced image processing techniques aimed at reconstructing lost woodblocks.
{"title":"Advanced Workflow for Extracting Characters from the Royal Woodblocks of the Nguyen Dynasty to Construct a Sino-Nom Dataset for Reconstructing Lost Woodblocks","authors":"Le Cong Thuong, Viet Nam Le, Thanh Ha Le, Thi Duyen Ngo","doi":"10.1016/j.daach.2025.e00448","DOIUrl":"10.1016/j.daach.2025.e00448","url":null,"abstract":"<div><div>The woodblock printing technique, first developed in China, enabled the large-scale production of texts and significantly advanced the spread of knowledge and literacy across many Asian countries for centuries. In Vietnam, the royal woodblocks of the Nguyen Dynasty are considered a national treasure. However, many of these woodblocks have been lost or damaged over time, making it imperative to develop a method for reconstructing them. Therefore, this paper proposes a data processing workflow capable of constructing a Sino-Nom character dataset from existing woodblock collections. The constructed dataset can then be used for reconstructing the lost woodblocks and for further in-depth analysis. Using the 3D collection of the Dai Nam Thuc Luc chronicle as an example, we have created a large Sino-Nom character dataset named SiNoC through our proposed workflow. The SiNoC dataset comprises 90,259 pairs of 3D and 2D Sino-Nom characters. This dataset serves as a foundation for deep learning models and advanced image processing techniques aimed at reconstructing lost woodblocks.</div></div>","PeriodicalId":38225,"journal":{"name":"Digital Applications in Archaeology and Cultural Heritage","volume":"38 ","pages":"Article e00448"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-08-11DOI: 10.1016/j.daach.2025.e00452
Lorenzo Cardarelli
Archaeological pottery documentation and study represents a crucial but time-consuming aspect of archaeology. While recent years have seen advances in digital documentation methods, vast amounts of legacy data remain locked in traditional publications. This paper introduces PyPotteryLens, an open-source framework that leverages deep learning to automate the digitisation and processing of archaeological pottery drawings from published sources. The system combines state-of-the-art computer vision models (YOLO for instance segmentation and EfficientNetV2 for classification) with an intuitive user interface, making advanced digital methods accessible to archaeologists regardless of technical expertise. The framework achieves over 97 % precision and recall in pottery detection and classification tasks, while reducing processing time by up to 5 × to 20 × compared to manual methods. Also, the system's modular architecture facilitates extension to other archaeological materials, while its standardised output format ensures long-term preservation and reusability of digitised data as well as solid basis for training machine learning algorithms.
{"title":"PyPotteryLens: An open-source deep learning framework for automated digitisation of archaeological pottery documentation","authors":"Lorenzo Cardarelli","doi":"10.1016/j.daach.2025.e00452","DOIUrl":"10.1016/j.daach.2025.e00452","url":null,"abstract":"<div><div>Archaeological pottery documentation and study represents a crucial but time-consuming aspect of archaeology. While recent years have seen advances in digital documentation methods, vast amounts of legacy data remain locked in traditional publications. This paper introduces <em>PyPotteryLens</em>, an open-source framework that leverages deep learning to automate the digitisation and processing of archaeological pottery drawings from published sources. The system combines state-of-the-art computer vision models (YOLO for instance segmentation and EfficientNetV2 for classification) with an intuitive user interface, making advanced digital methods accessible to archaeologists regardless of technical expertise. The framework achieves over 97 % precision and recall in pottery detection and classification tasks, while reducing processing time by up to 5 × to 20 × compared to manual methods. Also, the system's modular architecture facilitates extension to other archaeological materials, while its standardised output format ensures long-term preservation and reusability of digitised data as well as solid basis for training machine learning algorithms.</div></div>","PeriodicalId":38225,"journal":{"name":"Digital Applications in Archaeology and Cultural Heritage","volume":"38 ","pages":"Article e00452"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-08-09DOI: 10.1016/j.daach.2025.e00450
Evren Sertalp , Mehmet Sait Sütcü
Technological developments have led each discipline to produce new terms and techniques, providing different opportunities. Alongside virtual reality, hologram technology, and mixed reality, “augmented reality” has become a method frequently encountered in everyday life and an effective technological tool that can be applied to various fields. This technology combines the physical environment with the multiple media created on a computer. Consisting of 3D images, videos, and animations and offering audio narration, augmented reality has increased its importance in transmitting cultural heritage to future generations. The artefacts can be re-examined with augmented reality technology, and the necessary information can be obtained verbally and in writing. This study aims to create an augmented reality application to display an Ottoman galley whose physical remains have not survived to this day. This study uses the marker-based method for application to work on different mobile devices. This study has the importance of being the first study to create and display a 3D model of this galley. It also shows that the augmented reality application can make artefacts more perceptible and exciting and offers a new and technological alternative for displaying such artefacts.
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