Special Issue "Machine Learning and Deep Learning in Cultural Heritage"

Special Issue Editors

Dr. Susana Del Pozo
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Guest Editor
Cartographic and Land Engineering Department, Higher Polytechnic School of Avila, University of Salamanca, Hornos Caleros, 50 05003, Avila, Spain
Interests: multisensor multi-source data analysis; satellite imagery; geographic information systems; crop mapping; irrigation activity detection; remote sensing
Special Issues and Collections in MDPI journals
Dr. Jan Dirk Wegner
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Guest Editor
EcoVision Lab, ETH Zurich, Switzerland
Interests: deep learning for geospatial data analysis; large-scale machine learning; 3D computer vision
Special Issues and Collections in MDPI journals
Mr. Lloyd A. Courtenay
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Guest Editor
Cartographic and Land Engineering Department, Higher Polytechnic School of Avila, University of Salamanca, Hornos Caleros, 50 05003, Avila, Spain
Interests: data science; machine and deep learning; applied statistics; quaternary sciences; laser scanning; archaeology; taphonomy; human evolution; African heritage

Special Issue Information

Dear colleagues,

Digital and computer transformations not only lower costs for technologies and services, but also save time when improving final products and results. Specifically, machine and deep learning are two powerful tools that are transforming the face of many sectors, from medicine to physics, humanities, engineering, and many others. The components of machine learning prepare computers using a multitude of different algorithms to learn from large amounts of complex data to extract discriminative evidence for efficient decision-making. Algorithms currently excel in high-level feature extraction and pattern recognition tasks, such as image and natural language processing or classification. While they remain unknown to many, these algorithms now form part of our daily lives and are achieving revolutionary results in most fields of science.

In this context, it is essential to analyze the versatility and potential that these techniques have in the cultural heritage (CH) sector, in which the analysis of vast amounts of highly complex information is key. Diagnostics and preservation of CH are truly important to determine the state of conservation of historical monuments and buildings. This sector needs new solutions in order to objectively and efficiently manage the vast amount of information, usually in image or point cloud format, regarding the documentation and analysis of our cultural legacy. Efficient and accurate modern machine learning methods can be viewed as complementary to social sciences and humanities, providing powerful tools for analytical as well as didactical techniques. Machine learning excels in processing large, complex data, removing a significant degree of error which often the product of arguably subjective human input. In this regard, new challenges arise in order to apply computer technologies to the study and preservation of CH assets.

This Special Issue originates from the CIPA Symposium “CIPA 2019—Documenting the Past for a Better Future”, held in September 2019 in Avila, Spain. One of the main symposium’s scope is to bring together scientists, developers, and advanced users who apply sensors and methods in CH. Additionally, a special focus will be placed on the use of complex deep learning algorithms, capable of reaching the highest degrees of precision and resolution when processing both human-obtained data and images, which are typical of most CH projects. The most exciting and innovative papers related to machine and deep learning presented at the symposium will be selected to be extended and included in this Special Issue. In addition to this, we invite you to contribute to this Special Issue by submitting articles on your recent research, experimental work, reviews, and/or case studies related to the field of artificial intelligence applied to CH.

Relevant topics include, but are not limited to:

  • Robotic technologies applied to cultural heritage;
  • Monitoring heritage through time;
  • Cultural heritage diagnostics;
  • Impact of conservation tasks;
  • Virtual and augmented reality;
  • Automatic feature extraction in ancient buildings;
  • Image classification;
  • Improvements in artificial intelligence models and methods.

Dr. Susana Del Pozo
Dr. Jan Dirk Wegner
Mr. Lloyd A. Courtenay
Guest Editors

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 papers will be 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. ISPRS International Journal of Geo-Information 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 1000 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

  • cultural heritage
  • computer vision
  • artificial intelligence
  • big data
  • machine and deep learning
  • neural networks
  • feature extraction and classification
  • monitoring
  • conservation
  • statistics

Published Papers (2 papers)

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Research

Open AccessEditor’s ChoiceArticle
A Neural Networks Approach to Detecting Lost Heritage in Historical Video
ISPRS Int. J. Geo-Inf. 2020, 9(5), 297; https://doi.org/10.3390/ijgi9050297 - 05 May 2020
Abstract
Documenting Cultural Heritage through the extraction of 3D measures with photogrammetry is fundamental for the conservation of the memory of the past. However, when the heritage has been lost the only way to recover this information is the use of historical images from [...] Read more.
Documenting Cultural Heritage through the extraction of 3D measures with photogrammetry is fundamental for the conservation of the memory of the past. However, when the heritage has been lost the only way to recover this information is the use of historical images from archives. The aim of this study is to experiment with new ways to search for architectural heritage in video material and to save the effort of the operator in the archive in terms of efficiency and time. A workflow is proposed to automatically detect lost heritage in film footage using Deep Learning to find suitable images to process with photogrammetry for its 3D virtual reconstruction. The performance of the network was tested on two case studies considering different architectural scenarios, the Tour Saint Jacques which still exists for the tuning of the networks, and Les Halles to test the algorithms on a real case of an architecture which has been destroyed. Despite the poor quantity and low quality of the historical images available for the training of the network, it has been demonstrated that, with few frames, it was possible to reach the same results in terms of performance of a network trained on a large dataset. Moreover, with the introduction of new metrics based on time intervals the measure of the real time saving in terms of human effort was achieved. These findings represent an important innovation in the documentation of destroyed monuments and open new ways to recover information about the past. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Cultural Heritage)
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Open AccessArticle
Combining Deep Learning and Location-Based Ranking for Large-Scale Archaeological Prospection of LiDAR Data from The Netherlands
ISPRS Int. J. Geo-Inf. 2020, 9(5), 293; https://doi.org/10.3390/ijgi9050293 - 01 May 2020
Abstract
This paper presents WODAN2.0, a workflow using Deep Learning for the automated detection of multiple archaeological object classes in LiDAR data from the Netherlands. WODAN2.0 is developed to rapidly and systematically map archaeology in large and complex datasets. To investigate its practical value, [...] Read more.
This paper presents WODAN2.0, a workflow using Deep Learning for the automated detection of multiple archaeological object classes in LiDAR data from the Netherlands. WODAN2.0 is developed to rapidly and systematically map archaeology in large and complex datasets. To investigate its practical value, a large, random test dataset—next to a small, non-random dataset—was developed, which better represents the real-world situation of scarce archaeological objects in different types of complex terrain. To reduce the number of false positives caused by specific regions in the research area, a novel approach has been developed and implemented called Location-Based Ranking. Experiments show that WODAN2.0 has a performance of circa 70% for barrows and Celtic fields on the small, non-random testing dataset, while the performance on the large, random testing dataset is lower: circa 50% for barrows, circa 46% for Celtic fields, and circa 18% for charcoal kilns. The results show that the introduction of Location-Based Ranking and bagging leads to an improvement in performance varying between 17% and 35%. However, WODAN2.0 does not reach or exceed general human performance, when compared to the results of a citizen science project conducted in the same research area. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Cultural Heritage)
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