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Remote Sensing
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Application of Remote Sensing in Cultural Heritage Research II
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Application of Remote Sensing in Cultural Heritage Research II

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

Deadline for manuscript submissions: closed (26 April 2024) | Viewed by 5096

Special Issue Editors

Dr. George Alexis Ioannakis

E-Mail Website
Guest Editor
Athena Research and Innovation Centre/ILSP - Clepsydra Digitisation Lab, Xanthi, Greece
Interests: 3D digitisation; photogrammetry; AI; software engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Anestis Koutsoudis

E-Mail Website
Guest Editor
Athena Research and Innovation Centre/ILSP - Clepsydra Digitisation Lab, Xanthi, Greece
Interests: 3D digitisation; photogrammetry; real time computer graphics; software engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today’s world, remote sensing technologies play a crucial role in accurately documenting, restoring, monitoring, disseminating, and managing our cultural heritage. However, generating high-quality 3D assets from the CH domain remains a complex and challenging task that is heavily reliant on current research and technological advances across multiple scientific domains. These domains include remote sensing, artificial intelligence (AI), internet of things (IoT), geographic information systems (GIS), computer graphics, computer vision, and big data. Currently, significant basic and applied research efforts are focused on automating data collection procedures, data fusion, data handling, and management, all of which are advancing the current state of remote sensing applications in the CH domain.

The aim of this Special Issue is to explore various aspects of the multidisciplinary domains that employ remote sensing technologies to generate and interpret state-of-the-art 3D assets, providing solutions for a wide range of challenges related to cultural heritage. The objective is to gather research activities and case studies related to the following topics (among others):

  • The use of multispectral and hyperspectral data for 3D documentation and content analysis;
  • The integration of aerial and terrestrial multisensory data;
  • Autonomous aerial data collection for the 3D documentation of CH sites using photogrammetric/LiDAR techniques;
  • Multimodal monitoring and novelty detection of CH sites;
  • The evaluation of commercial and experimental aerial/terrestrial data collection systems based on use-case scenarios;
  • Specification of requirements and designs for large-scale 3D documentation projects;
  • Monitoring of risks, restoration, and management of CH sites;
  • Geospatial and climate analysis for the protection of CH sites;
  • Content analysis of CH assets based on machine learning techniques;
  • Methodologies for visualizing and disseminating big data;
  • Review articles that cover one or more of the above topics are also welcome.

We extend an invitation and encourage experts who specialize in the aforementioned fields to submit their contributions.

Dr. George Alexis Ioannakis
Dr. Anestis Koutsoudis
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

  • 3D digitisation
  • remote sensing
  • multispectral/hyperspectral data
  • geospatial analysis
  • machine learning
  • photogrammetry
  • lidar
  • aerial/terrestrial data collection
  • restoration/preservation

Related Special Issue

Published Papers (3 papers)

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Research

30 pages, 23312 KiB  
Article
A Multisensory Analysis of the Moisture Course of the Cave of Altamira (Spain): Implications for Its Conservation
by Vicente Bayarri, Alfredo Prada, Francisco García, Carmen De Las Heras and Pilar Fatás
Remote Sens. 2024, 16(1), 197; https://doi.org/10.3390/rs16010197 - 03 Jan 2024
Cited by 1 | Viewed by 1201
Abstract
This paper addresses the conservation problems of the cave of Altamira, a UNESCO World Heritage Site in Santillana del Mar, Cantabria, Spain, due to the effects of moisture and water inside the cave. The study focuses on the description of methods for estimating [...] Read more.
This paper addresses the conservation problems of the cave of Altamira, a UNESCO World Heritage Site in Santillana del Mar, Cantabria, Spain, due to the effects of moisture and water inside the cave. The study focuses on the description of methods for estimating the trajectory and zones of humidity from the external environment to its eventual dripping on valuable cave paintings. To achieve this objective, several multisensor remote sensing techniques, both aerial and terrestrial, such as 3D laser scanning, a 2D ground penetrating radar, photogrammetry with unmanned aerial vehicles, and high-resolution terrestrial techniques are employed. These tools allow a detailed spatial analysis of the moisture and water in the cave. The paper highlights the importance of the dolomitic layer in the cave and how it influences the preservation of the ceiling, which varies according to its position, whether it is sealed with calcium carbonate, actively dripping, or not dripping. In addition, the crucial role of the central fracture and the areas of direct water infiltration in this process is examined. This research aids in understanding and conserving the site. It offers a novel approach to water-induced deterioration in rock art for professionals and researchers. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Cultural Heritage Research II)
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18 pages, 13591 KiB  
Article
Remotely Sensing the Invisible—Thermal and Magnetic Survey Data Integration for Landscape Archaeology
by Jegor K. Blochin, Elena A. Pavlovskaia, Timur R. Sadykov and Gino Caspari
Remote Sens. 2023, 15(20), 4992; https://doi.org/10.3390/rs15204992 - 17 Oct 2023
Viewed by 1049
Abstract
Archaeological landscapes can be obscured by environmental factors, rendering conventional visual interpretation of optical data problematic. The absence of evidence can lead to seemingly empty locations and isolated monuments. This, in turn, influences the cultural–historical interpretation of archaeological sites. Here, we assess the [...] Read more.
Archaeological landscapes can be obscured by environmental factors, rendering conventional visual interpretation of optical data problematic. The absence of evidence can lead to seemingly empty locations and isolated monuments. This, in turn, influences the cultural–historical interpretation of archaeological sites. Here, we assess the potential of integrating thermal and magnetic remote sensing methods in the detection and mapping of buried archaeological structures. The area of interest in an alluvial plain in Tuva Republic makes the application of standard methods like optical remote sensing and field walking impractical, as natural vegetation features effectively hide anthropogenic structures. We combined drone-based aerial thermography and airborne and ground-based magnetometry to establish an approach to reliably identifying stone structures concealed within alluvial soils. The data integration led to the discovery of nine buried archaeological structures in proximity to an Early Iron Age royal tomb, shedding light on ritual land use continuity patterns. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Cultural Heritage Research II)
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21 pages, 3709 KiB  
Article
Exploring Deep Learning Models on GPR Data: A Comparative Study of AlexNet and VGG on a Dataset from Archaeological Sites
by Merope Manataki, Nikos Papadopoulos, Nikolaos Schetakis and Alessio Di Iorio
Remote Sens. 2023, 15(12), 3193; https://doi.org/10.3390/rs15123193 - 20 Jun 2023
Viewed by 1680
Abstract
This comparative study evaluates the performance of three popular deep learning architectures, AlexNet, VGG-16, and VGG-19, on a custom-made dataset of GPR C-scans collected from several archaeological sites. The introduced dataset has 15,000 training images and 3750 test images assigned to three classes: [...] Read more.
This comparative study evaluates the performance of three popular deep learning architectures, AlexNet, VGG-16, and VGG-19, on a custom-made dataset of GPR C-scans collected from several archaeological sites. The introduced dataset has 15,000 training images and 3750 test images assigned to three classes: Anomaly, Noise, and Structure. The aim is to assess the performance of the selected architectures applied to the custom dataset and examine the potential gains of using deeper and more complex architectures. Further, this study aims to improve the training dataset using augmentation techniques. For the comparisons, learning curves, confusion matrices, precision, recall, and f1-score metrics are employed. The Grad-CAM technique is also used to gain insights into the models’ learning. The results suggest that using more convolutional layers improves overall performance. Further, augmentation techniques can also be used to increase the dataset volume without causing overfitting. In more detail, the best-obtained model was trained using VGG-19 architecture and the modified dataset, where the training samples were raised to 60,000 images through augmentation techniques. This model reached a classification accuracy of 94.12% on an evaluation set with 170 unseen data. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Cultural Heritage Research II)
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