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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: 30 November 2024 | Viewed by 7697

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

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Related Special Issue

Published Papers (6 papers)

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Research

20 pages, 7800 KiB  
Article
Hydraulic Risk Assessment on Historic Masonry Bridges Using Hydraulic Open-Source Software and Geomatics Techniques: A Case Study of the “Hannibal Bridge”, Italy
by Ahmed Kamal Hamed Dewedar, Donato Palumbo and Massimiliano Pepe
Remote Sens. 2024, 16(16), 2994; https://doi.org/10.3390/rs16162994 - 15 Aug 2024
Abstract
This paper investigates the impact of flood-induced hydrodynamic forces and high discharge on the masonry arch “Hannibal Bridge” (called “Ponte di Annibale” in Italy) using the Hydraulic Engineering Center’s River Analysis Simulation (HEC-RAS) v6.5.0. hydraulic numerical method, incorporating Unmanned Aerial Vehicle (UAV) photogrammetry [...] Read more.
This paper investigates the impact of flood-induced hydrodynamic forces and high discharge on the masonry arch “Hannibal Bridge” (called “Ponte di Annibale” in Italy) using the Hydraulic Engineering Center’s River Analysis Simulation (HEC-RAS) v6.5.0. hydraulic numerical method, incorporating Unmanned Aerial Vehicle (UAV) photogrammetry and aerial Light Detection and Ranging (LIDAR) data for visual analysis. The research highlights the highly transient behavior of fast flood flows, particularly when carrying debris, and their effect on bridge superstructures. Utilizing a Digital Elevation Model to extract cross-sectional and elevation data, the research examined 23 profiles over 800 m of the river. The results indicate that the maximum allowable water depth in front of the bridge is 4.73 m, with a Manning’s coefficient of 0.03 and a longitudinal slope of 9 m per kilometer. Therefore, a novel method to identify the risks through HEC-RAS modeling significantly improves the conservation of masonry bridges by providing precise topographical and hydrological data for accurate simulations. Moreover, the detailed information obtained from LIDAR and UAV photogrammetry about the bridge’s materials and structures can be incorporated into the conservation models. This comprehensive approach ensures that preservation efforts are not only addressing the immediate hydrodynamic threats but are also informed by a thorough understanding of the bridge’s structural and material conditions. Understanding rating curves is essential for water management and flood forecasting, with the study confirming a Manning roughness coefficient of 0.03 as suitable for smooth open-channel flows and emphasizing the importance of geomorphological conditions in hydraulic simulation. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Cultural Heritage Research II)
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21 pages, 29727 KiB  
Article
Remote Sensing Integration to Geohazard Management at the Castle-Monastery of Panagia Spiliani, Nisyros Island, Greece
by Marinos Vassilis, Farmakis Ioannis, Chatzitheodosiou Themistoklis, Papouli Dimitra, Stoumpos Georgios, Prountzopoulos Georgios and Karantanellis Efstratios
Remote Sens. 2024, 16(15), 2768; https://doi.org/10.3390/rs16152768 - 29 Jul 2024
Viewed by 380
Abstract
The Holy Monastery of Panagia Spiliani is an important religious monument of the Aegean islands. The monastery is built on a steep rocky hill in the Castle of Mandraki on Nisyros island. On the slopes of the foundation area of the monastery, landslides [...] Read more.
The Holy Monastery of Panagia Spiliani is an important religious monument of the Aegean islands. The monastery is built on a steep rocky hill in the Castle of Mandraki on Nisyros island. On the slopes of the foundation area of the monastery, landslides have occurred in the past, mainly rockfalls and slides, while the risk of new similar phenomena in the future is high. To assist the geohazard assessment and mitigation design works, a combined survey using Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) photogrammetry was implemented. Besides capturing the detailed morphology within high-resolution 3D point clouds, the main engineering geological units were identified on the slopes, while critical structural ground elements and unstable blocks were mapped in detail. These were quantified in terms of geotechnical parameters, and the engineering geological model of the hill was finalised and presented in an engineering geological map and cross sections. The mitigation measures are targeted towards the stabilisation of the wider area of the upper slope, hence the stability of the monastery and its surroundings risk elements, as well as the support of specific, large- to small-scale unstable rock blocks on the whole slope area, securing accessibility to the main beach of the village. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Cultural Heritage Research II)
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22 pages, 14976 KiB  
Article
Missing Region Completion Network for Large-Scale Laser-Scanned Point Clouds: Application to Transparent Visualization of Cultural Heritage
by Weite Li, Jiao Pan, Kyoko Hasegawa, Liang Li and Satoshi Tanaka
Remote Sens. 2024, 16(15), 2758; https://doi.org/10.3390/rs16152758 - 28 Jul 2024
Viewed by 617
Abstract
The digital documentation and analysis of cultural heritage increasingly rely on high-precision three-dimensional point cloud data, which often suffers from missing regions due to limitations in acquisition conditions, hindering subsequent analyses and applications. Point cloud completion techniques, by predicting and filling these missing [...] Read more.
The digital documentation and analysis of cultural heritage increasingly rely on high-precision three-dimensional point cloud data, which often suffers from missing regions due to limitations in acquisition conditions, hindering subsequent analyses and applications. Point cloud completion techniques, by predicting and filling these missing regions, are vital for restoring the integrity of cultural heritage structures, enhancing restoration accuracy and efficiency. In this paper, for challenges in processing large-scale cultural heritage point clouds, particularly the slow processing speed and visualization impairments from uneven point density during completion, we propose a point cloud completion employing centroid-based voxel feature extraction, which significantly accelerates feature extraction for massive point clouds. Coupled with an efficient upsampling module, it achieves a uniform point distribution. Experimental results show that the proposed method matches SOTA performance in completion accuracy while surpassing in point density uniformity, demonstrating capability in handling larger-scale point cloud data, and accelerating the processing of voluminous point clouds. In general, the proposed method markedly enhances the efficiency and quality of large-scale point cloud completion, holding significant value for the digital preservation and restoration of cultural heritage. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Cultural Heritage Research II)
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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 - 3 Jan 2024
Cited by 3 | Viewed by 1636
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
Cited by 1 | Viewed by 1352
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
Cited by 2 | Viewed by 2097
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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