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Heritage
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Advanced 2D and 3D Modeling Techniques and AI Applications for...
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Advanced 2D and 3D Modeling Techniques and AI Applications for Archaeological Site Documentation and Preservation

A special issue of Heritage (ISSN 2571-9408).

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2348

Special Issue Editor

Dr. Elisa Mariarosaria Farella

E-Mail Website
Guest Editor
3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), Via Sommarive, 18, 38123 Trento, Italy
Interests: cultural heritage 3D documentation and valorization; data and sensors fusion; automated 3D modelling; AR/VR in the geomatics sector; geoprocessing of aerial and satellite imagery; application of AI methods to heritage and geomatics topics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to our Special Issue on “Advanced 2D and 3D Modeling Techniques and AI Applications for Archaeological Site Documentation and Preservation”. Recent advancements in digital technologies, and 2D and 3D data capturing and processing techniques, as well as the rise of Artificial Intelligence solutions in archaeology and Cultural Heritage (CH) studies, have reshaped traditional approaches for recording, documenting, and interpreting archaeological sites.

This Special Issue aims to explore how these emerging technologies can open new perspectives in archaeological site management, interpretation, preservation, and dissemination. This Issue encourages groundbreaking and interdisciplinary research to showcase the potential of the proposed approaches to take digital practices one step further.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Automated 2D and 3D documentation workflows;
  • AI-driven interpretation and reconstruction of archaeological sites;
  • Data and sensor integration and fusion;
  • Generative and procedural modelling in heritage contexts;
  • Remote sensing and UAV-based site analysis;
  • Heritage Digital Twins and site monitoring.

We look forward to receiving your contributions.

Dr. Elisa Mariarosaria Farella
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Heritage is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • reality-based modelling
  • documentation
  • automated data processing
  • multi-sensor fusion
  • AI
  • digital twins
  • spatial analysis
  • interpretation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

22 pages, 249676 KB  
Article
AI- and AR-Assisted Reactivation of Chinese Paper Cutting Using Temple Arts and Ancient Paintings
by Naai-Jung Shih and Yan-Ting Chen
Heritage 2026, 9(4), 150; https://doi.org/10.3390/heritage9040150 - 7 Apr 2026
Viewed by 597
Abstract
Traditional Chinese paper cutting represents an important intangible cultural heritage. Can artificial intelligence (AI) reactivate the heritage in a new style? The aim of this study was to use AI to reactivate temple arts and paintings by converting them into the style of [...] Read more.
Traditional Chinese paper cutting represents an important intangible cultural heritage. Can artificial intelligence (AI) reactivate the heritage in a new style? The aim of this study was to use AI to reactivate temple arts and paintings by converting them into the style of traditional Chinese paper cuttings. Thirty sets of old images taken 18 years ago and 10 images of ancient paintings from the National Palace Museum were restyled in Nano Banana (Pro)®. Related design elements included integrated isolated parts, visual depth, details, and solid and void alternation. Three-dimensional stone and wood sculptures were reconstructed using Rodin® or Meshy® and converted into AR models in Sketchfab®. From the generated 2D images and their 3D representations, a reactivated style of Chinese paper cutting was developed that can be interacted with in the AR smartphone platform or RP in the physical world. Approximately 370 images were regenerated, and 167 versions of models were reconstructed. AI should be considered part of culture. Rethinking traditional folk art highlights demand for the cross-reference and cross-reactivation of heterogeneous art forms. This AI model interprets novel 3D structural and visual details and creates a unique 2D and 3D identity for each subject. Full article
(This article belongs to the Special Issue Advanced 2D and 3D Modeling Techniques and AI Applications for Archaeological Site Documentation and Preservation)
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23 pages, 6950 KB  
Article
Under-Canopy Archaeological Mapping Using LiDAR Data and AI Methods
by Gabriele Mazzacca and Fabio Remondino
Heritage 2026, 9(4), 134; https://doi.org/10.3390/heritage9040134 - 27 Mar 2026
Viewed by 613
Abstract
Airborne laser scanning (ALS) and UAV-mounted LiDAR sensors have become well-established tools for identifying and mapping archaeological features across varying scales and contexts. Numerous algorithms have been developed over the years for generating Digital Terrain or Features Models (DTMs/DFMs), which provide an accurate [...] Read more.
Airborne laser scanning (ALS) and UAV-mounted LiDAR sensors have become well-established tools for identifying and mapping archaeological features across varying scales and contexts. Numerous algorithms have been developed over the years for generating Digital Terrain or Features Models (DTMs/DFMs), which provide an accurate representation of the ground or structures’ surface, serving as the foundation for subsequent archaeological analyses. In this study, we report the developed multi-level multi-resolution (MLMR) methodology, based on machine/deep learning methods, for DFM generation through point cloud semantic segmentation. The work also compares different approaches and the impact of the resolution on their performance. To this end, each approach’s performance is evaluated with a series of quantitative and qualitative analyses, with an eye on hardware limitations and time constraints. Three test sites from Mediterranean and Alpine environments, with manually annotated ground truth data, are used for the evaluation of each methodological approach. Full article
(This article belongs to the Special Issue Advanced 2D and 3D Modeling Techniques and AI Applications for Archaeological Site Documentation and Preservation)
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