Artificial Intelligence in Heritage Science

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

Deadline for manuscript submissions: closed (13 March 2021) | Viewed by 5461

Special Issue Editor


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Guest Editor
Researcher at ATHENA Research & Innovation Center, Cultural and Educational Technology Institute, ATHENA - Research and Innovation Centre in Information, Communication and Knowledge Technologies, University Campus at Kimmeria, PO BOX 159, GR-67100 Xanthi, Greece
Interests: computer science; multimedia; cultural technology; digital humanities; heritage science
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Special Issue Information

Dear Colleagues,

The advent of the deep learning era brings about an increasing interest for artificial intelligence applications in diverse domains. Artificial Intelligence (AI) and archaeology, or Cultural Heritage (CH) in general, have already crossed paths in a number of occasions. From scientific visualization and data representation, to knowledge management, empowerment of research, digital applications for museums, sites and tourism, AI is expected to be ubiquitous and game-changing in the following decades. AI has already successfully appeared in a diverse set of CH applications, including element/mineral identification, virtual museums, historical document analysis, natural language processing, semantics and knowledge extraction, automated processes in digitization, recommenders, storytelling and personalization. This session aims to attract researchers in this strongly cross-disciplinary domain and give floor to the dialogue between AI and CH, towards the digital heritage of the future.

Topics of interest in this session include, but are not limited to:
- AI in digital archaeology, digitization and on-site documentation
- AI in digital cultural content/object analysis
- AI in content – based classification and retrieval
- AI in archaeometry and data analysis
- AI in natural language processing and CH applications
- AI in semantics and knowledge representation
- AI in museums and cultural tourism
- AI in virtual systems for education and tourism
- Computational archaeology
- Intelligent methods in spatial and temporal analysis
- AI and simulations in archaeology and CH
- Intelligent crowdsourcing approaches

Dr. George Pavlidis
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 1600 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

  • artificial intelligence
  • machine learning
  • digital humanities

Published Papers (1 paper)

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Research

19 pages, 12221 KiB  
Article
Feature-Based Point Cloud-Based Assessment of Heritage Structures for Nondestructive and Noncontact Surface Damage Detection
by Richard L. Wood and Mohammad Ebrahim Mohammadi
Heritage 2021, 4(2), 775-793; https://doi.org/10.3390/heritage4020043 - 11 May 2021
Cited by 7 | Viewed by 2844
Abstract
Assessment and evaluation of damage in cultural heritage structures are conducted primarily using nondestructive and noncontact methods. One common deployment is laser scanners or ground-based lidar scanners that produce a point cloud containing information at the centimeter to the millimeter level. This type [...] Read more.
Assessment and evaluation of damage in cultural heritage structures are conducted primarily using nondestructive and noncontact methods. One common deployment is laser scanners or ground-based lidar scanners that produce a point cloud containing information at the centimeter to the millimeter level. This type of data allows for detecting surface damage, defects, cracks, and other anomalies based only on geometric surface descriptors using a single dataset, which does not rely on a change detection approach. Moreover, geometric features are not influenced by color, which is essential for heritage structures because they are nonuniform in color due to anthropologic and environmental effects (e.g., painting or moisture). In this work, a damage detection method developed based on local geometric features is evaluated and expanded for crack detection within the example fresco walls of Sala degli Elementi in the Palazzo Vecchio. The workflow’s performance is then compared in a qualitative manner to that of manual crack mapping results identified using images. Full article
(This article belongs to the Special Issue Artificial Intelligence in Heritage Science)
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