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Remote Sensing
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Digitization and Visualization in Cultural Heritage
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Digitization and Visualization in Cultural Heritage

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 17195

Special Issue Editors

Dr. Nikos Grammalidis

E-Mail Website
Guest Editor
Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece
Interests: computer vision; machine learning and artificial intelligence; multi-dimensional signal processing; intelligent systems and applications; environmental informatics and remote sensing; ICT for civil protection
Special Issues, Collections and Topics in MDPI journals
Dr. Kosmas Dimitropoulos

E-Mail Website
Guest Editor
Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece
Interests: artificial intelligence; computer vision; human-computer interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Anastasios Doulamis

E-Mail Website
Guest Editor
Photogrammetry and Computer Vision Laboratory, National Technical University of Athens, 15773 Athens, Greece
Interests: image processing; computer vision; robotic systems; deep machine learning; machine learning; Markovian models; signal processing and pattern analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cultural heritage, in both its tangible and intangible forms, is an important factor for tourism, social cohesion, and local economic development. This is evident not only in the worldwide listed UNESCO world heritage sites, but also in off-the-beaten-track areas with sites or cultural heritage assets of a regional importance level, which are not—yet—internationally recognized. The impact of economic crisis, epidemics, and other demographic, structural, immigration, etc. problems creates a strong need to leverage digital cultural assets for economic development. The latest advances in remote sensing, digitization, machine learning, visualization, and simulation offer new opportunities for wider online accessibility and interlinking of digital cultural collections/assets, as well as their exploitation and re-use for added-value products and services in many application areas (e.g., education, entertainment) using new business models.

The purpose of this Special Issue is to bring together engineers, data scientists, researchers, and practitioners to present new academic research and commercial solutions for the digitization, documentation, conservation, restoration, interlinking, visualization, exploitation, and re-use of tangible and intangible cultural heritage. The Special Issue will gather original research papers in the field, covering new theories, algorithms, systems, as well as new implementations and applications incorporating state-of-the-art techniques. Emphasis will be placed on novel approaches and technologies that support faster, better, and cheaper digitization and presentation of heritage assets. Review articles and works on performance evaluation are also solicited.

Guest Editors

Dr. Nikos Grammalidis
Dr. Kosmas Dimitropoulos
Dr. Anastasios Doulamis
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 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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • Tangible and intangible cultural heritage
  • Digitization of cultural heritage assets
  • Semantic analysis and e-documentation
  • 3D and immersive visualization, including Virtual, Augmented, and Mixed Reality
  • Preservation and protection of cultural heritage
  • Digital cultural heritage applications and services

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

Published Papers (5 papers)

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Research

21 pages, 19437 KiB  
Article
Semantic Segmentation for Digital Archives of Borobudur Reliefs Based on Soft-Edge Enhanced Deep Learning
by Shenyu Ji, Jiao Pan, Liang Li, Kyoko Hasegawa, Hiroshi Yamaguchi, Fadjar I. Thufail, Brahmantara, Upik Sarjiati and Satoshi Tanaka
Remote Sens. 2023, 15(4), 956; https://doi.org/10.3390/rs15040956 - 9 Feb 2023
Cited by 5 | Viewed by 2857
Abstract
Segmentation and visualization of three-dimensional digital cultural heritage are important analytical tools for the intuitive understanding of content. In this paper, we propose a semantic segmentation and visualization framework that automatically classifies carved items (people, buildings, plants, etc.) in cultural heritage reliefs. We [...] Read more.
Segmentation and visualization of three-dimensional digital cultural heritage are important analytical tools for the intuitive understanding of content. In this paper, we propose a semantic segmentation and visualization framework that automatically classifies carved items (people, buildings, plants, etc.) in cultural heritage reliefs. We also apply our method to the bas-reliefs of Borobudur Temple, a UNESCO World Heritage Site in Indonesia. The difficulty in relief segmentation lies in the fact that the boundaries of each carved item are formed by indistinct soft edges, i.e., edges with low curvature. This unfavorable relief feature leads the conventional methods to fail to extract soft edges, whether they are three-dimensional methods classifying a three-dimensional scanned point cloud or two-dimensional methods classifying pixels in a drawn image. To solve this problem, we propose a deep-learning-based soft edge enhanced network to extract the semantic labels of each carved item from multichannel images that are projected from the three-dimensional point clouds of the reliefs. The soft edges in the reliefs can be clearly extracted using our novel opacity-based edge highlighting method. By mapping the extracted semantic labels into three-dimensional points of the relief data, the proposed method provides comprehensive three-dimensional semantic segmentation results of the Borobudur reliefs. Full article
(This article belongs to the Special Issue Digitization and Visualization in Cultural Heritage)
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19 pages, 6001 KiB  
Article
Towards Classification of Architectural Styles of Chinese Traditional Settlements Using Deep Learning: A Dataset, a New Framework, and Its Interpretability
by Qing Han, Chao Yin, Yunyuan Deng and Peilin Liu
Remote Sens. 2022, 14(20), 5250; https://doi.org/10.3390/rs14205250 - 20 Oct 2022
Cited by 12 | Viewed by 3221
Abstract
The classification of architectural style for Chinese traditional settlements (CTSs) has become a crucial task for developing and preserving settlements. Traditionally, the classification of CTSs primarily relies on manual work, which is inefficient and time consuming. Inspired by the tremendous success of deep [...] Read more.
The classification of architectural style for Chinese traditional settlements (CTSs) has become a crucial task for developing and preserving settlements. Traditionally, the classification of CTSs primarily relies on manual work, which is inefficient and time consuming. Inspired by the tremendous success of deep learning (DL), some recent studies attempted to apply DL networks such as convolution neural networks (CNNs) to achieve automated classification of the architecture styles. However, these studies suffer overfitting problems of the CNNs, leading to inferior classification performance. Moreover, most of the studies apply the CNNs as a black box providing limited interpretability. To address these limitations, a new DL classification framework is proposed in this study to overcome the overfitting problem by transfer learning and learning-based data augmentation technique (i.e., AutoAugment). Furthermore, we also employ class activation map (CAM) visualization technique to help understand how the CNN classifiers work to abstract patterns from the input. Specifically, due to a lack of architectural style datasets for the CTSs, a new annotated dataset is first established with six representative classes. Second, several representative CNNs are leveraged to benchmark the new dataset. Third, to address the overfitting problem of the CNNs, a new DL framework is proposed which combines transfer learning and AutoAugment to improve the classification performance. Extensive experiments are conducted on the new dataset to demonstrate the effectiveness of our framework. The proposed framework achieves much better performance than baselines, greatly mitigating the overfitting problem. Additionally, the CAM visualization technique is harnessed to explain what and how the CNN classifiers implicitly learn for recognizing a specified architectural style. Full article
(This article belongs to the Special Issue Digitization and Visualization in Cultural Heritage)
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32 pages, 37407 KiB  
Article
Achieving Universal Accessibility through Remote Virtualization and Digitization of Complex Archaeological Features: A Graphic and Constructive Study of the Columbarios of Merida
by Jorge Alberto Ramos Sánchez, Pablo Alejandro Cruz Franco and Adela Rueda Márquez de la Plata
Remote Sens. 2022, 14(14), 3319; https://doi.org/10.3390/rs14143319 - 10 Jul 2022
Cited by 16 | Viewed by 2558
Abstract
Currently, there are heritage assets that have been extensively studied and documented, but sometimes this information is not fully accessible to users. The aim of this research was to establish protocols and methodologies to promote collaborative work between the disciplines of architecture, restoration, [...] Read more.
Currently, there are heritage assets that have been extensively studied and documented, but sometimes this information is not fully accessible to users. The aim of this research was to establish protocols and methodologies to promote collaborative work between the disciplines of architecture, restoration, and archaeology, through the results offered by Building Information Modelling (BIM) tools, and to use them for Heritage Building Information Modelling (HBIM). The methodology applied employed data collection with fast and low-cost tools (UAV) to subsequently generate a photogrammetric survey to serves as the basis for three-dimensional modelling. In this parametric model we implement all the information obtained by professionals from different disciplines, which also serves as a means to publicise and disseminate the heritage asset. The case study was the archaeological site of Columbarios, located in Mérida, a UNESCO World Heritage City. We obtained an effective interdisciplinary work methodology for heritage management under a collaborative BIM environment. The study has allowed us to make the archaeological remains available to visit from anywhere in the world through Augmented Reality (AR) and Virtual Reality (VR) technology. Full article
(This article belongs to the Special Issue Digitization and Visualization in Cultural Heritage)
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19 pages, 31593 KiB  
Article
Integrated High-Definition Visualization of Digital Archives for Borobudur Temple
by Jiao Pan, Liang Li, Hiroshi Yamaguchi, Kyoko Hasegawa, Fadjar I. Thufail, Brahmantara and Satoshi Tanaka
Remote Sens. 2021, 13(24), 5024; https://doi.org/10.3390/rs13245024 - 10 Dec 2021
Cited by 3 | Viewed by 4140
Abstract
The preservation and analysis of tangible cultural heritage sites have attracted enormous interest worldwide. Recently, establishing three-dimensional (3D) digital archives has emerged as a critical strategy for the permanent preservation and digital analysis of cultural sites. For extant parts of cultural sites, 3D [...] Read more.
The preservation and analysis of tangible cultural heritage sites have attracted enormous interest worldwide. Recently, establishing three-dimensional (3D) digital archives has emerged as a critical strategy for the permanent preservation and digital analysis of cultural sites. For extant parts of cultural sites, 3D scanning is widely used for efficient and accurate digitization. However, in many historical sites, many parts that have been damaged or lost by natural or artificial disasters are unavailable for 3D scanning. The remaining available data sources for these destroyed parts are photos, computer-aided design (CAD) drawings, written descriptions, etc. In this paper, we achieve an integrated digital archive of a UNESCO World Heritage site, namely, the Borobudur temple, in which buried reliefs and internal foundations are not available for 3D scanning. We introduce a digitizing framework to integrate three different kinds of data sources and to create a unified point-cloud-type digital archive. This point-based integration enables us to digitally record the entire 3D structure of the target cultural heritage site. Then, the whole site is visualized by stochastic point-based rendering (SPBR) precisely and comprehensibly. The proposed framework is widely applicable to other large-scale cultural sites. Full article
(This article belongs to the Special Issue Digitization and Visualization in Cultural Heritage)
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25 pages, 14379 KiB  
Article
Connecting Images through Sources: Exploring Low-Data, Heterogeneous Instance Retrieval
by Dimitri Gominski, Valérie Gouet-Brunet and Liming Chen
Remote Sens. 2021, 13(16), 3080; https://doi.org/10.3390/rs13163080 - 5 Aug 2021
Cited by 6 | Viewed by 2341
Abstract
Along with a new volume of images containing valuable information about our past, the digitization of historical territorial imagery has brought the challenge of understanding and interconnecting collections with unique or rare representation characteristics, and sparse metadata. Content-based image retrieval offers a promising [...] Read more.
Along with a new volume of images containing valuable information about our past, the digitization of historical territorial imagery has brought the challenge of understanding and interconnecting collections with unique or rare representation characteristics, and sparse metadata. Content-based image retrieval offers a promising solution in this context, by building links in the data without relying on human supervision. However, while the latest propositions in deep learning have shown impressive results in applications linked to feature learning, they often rely on the hypothesis that there exists a training dataset matching the use case. Increasing generalization and robustness to variations remains an open challenge, poorly understood in the context of real-world applications. Introducing the alegoria benchmark, containing multi-date vertical and oblique aerial digitized photography mixed with more modern street-level pictures, we formulate the problem of low-data, heterogeneous image retrieval, and propose associated evaluation setups and measures. We propose a review of ideas and methods to tackle this problem, extensively compare state-of-the-art descriptors and propose a new multi-descriptor diffusion method to exploit their comparative strengths. Our experiments highlight the benefits of combining descriptors and the compromise between absolute and cross-domain performance. Full article
(This article belongs to the Special Issue Digitization and Visualization in Cultural Heritage)
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