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Multi-Data Integration in Near-Surface Geophysics and Close Range Remote Sensing Applied to 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: 26 May 2024 | Viewed by 8928

Special Issue Editors


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Guest Editor
Institute of Heritage Science, National Research Council (ISPC CNR), I-85050 Tito, Potenza, Italy
Interests: GIS; geocomputation; remote sensing; geophysics; cultural heritage; landscape archaeology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, data coming from near-surface geophysics and close-range remote sensing have become fundamental instruments in the field of cultural heritage (CH). In fact, in the literature, it is possible to find different types of study cases and approaches ranging from the detection of archaeological proxy indicators to automatic pattern extraction for applications in diverse cultural heritage assets and features, including historical masonry structures, architectural surfaces and frescoes.

However, even though data integration is a well-established practice for remote sensing applied to CH, concerning near-surface geophysics and close-range remote sensing, this integration often still remains at a basic level, involving just pixel-by-pixel composition or basic analysis.

Nevertheless, today, the analytical toolkits available (from spatial to intelligent analysis) can lead to a deeper and effective multi-data integration, able to improve results and create more effective methods in the application field of cultural heritage

In this Special Issue, contributions presenting relevant analytical approaches for multi-data integration in near-surface geophysics and close-range remote sensing (spatial analysis, spatial autocorrelation application, machine learning, deep learning, cellular automata, and agent-based approaches) in the field of cultural heritage are welcome.

Authors are invited to submit:

  • papers showing new or consolidated types of advanced analytical data integration for the different types of cultural heritage;
  • reviews of the limitations and advantages of the existing methods in this relevant research field.

Dr. Maria Danese
Prof. Dr. Nicola Masini
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

  • near-surface geophysics
  • close-range remote sensing
  • multi-data integration
  • GIS
  • spatial analysis
  • machine learning
  • deep learning
  • cultural heritage

Published Papers (4 papers)

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Research

25 pages, 10052 KiB  
Article
A Machine-Learning-Assisted Classification Algorithm for the Detection of Archaeological Proxies (Cropmarks) Based on Reflectance Signatures
by Athos Agapiou and Elias Gravanis
Remote Sens. 2024, 16(10), 1705; https://doi.org/10.3390/rs16101705 - 11 May 2024
Viewed by 448
Abstract
The detection of subsurface archaeological remains using a range of remote sensing methods poses several challenges. Recent studies regarding the detection of archaeological proxies like those of cropmarks highlight the complexity of the phenomenon. In this work, we present three different methods, and [...] Read more.
The detection of subsurface archaeological remains using a range of remote sensing methods poses several challenges. Recent studies regarding the detection of archaeological proxies like those of cropmarks highlight the complexity of the phenomenon. In this work, we present three different methods, and associated indices, for identifying stressed reflectance signatures indicating buried archaeological remains, based on a dataset of measured ground spectroradiometric reflectance. Several spectral profiles between the visible and near-infrared parts of the spectrum were taken in a controlled environment in Cyprus during 2011–2012 and are re-used in this study. The first two (spectral) methods are based on a suitable analysis of the spectral signatures in (1) the visible part of the spectrum, in particular in the neighborhood of 570 nm, and (2) the red edge part of the spectrum, in the neighborhood of 730 nm. Machine learning (decision trees) allows for the deduction of suitable wavelengths to focus on in order to formulate the proposed indices and the associated classification criteria (decision boundaries) that can enhance the detection probability of stressed vegetation. Noise in the signal is taken into account by simulating reflectance signatures perturbed by white noise. Applying decision tree classification on the ensemble of simulations and basic statistical analysis, we refine the formulation of the indices and criteria for the noisy signatures. The success rate of the proposed methods is over 90%. The third method rests on the estimation of vegetation/canopy reflectance parameters through inversion of the physical-based PROSAIL reflectance model and the associated classification through machine learning methods. The obtained results provide further insights into the formation of stress vegetation that occurred due to the presence of shallow buried archaeological remains, which are well aligned with physical-based models and existing empirical knowledge. To the best of the authors’ knowledge, this is the first study demonstrating the usefulness of radiative transfer models such as PROSAIL for understanding the formation of cropmarks. Similar studies can support future research directions towards the development of regional remote sensing methods and algorithms if systematic observations are adequately dispersed in space and time. Full article
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39 pages, 43506 KiB  
Article
Assessing Conservation Conditions at La Fortaleza de Kuelap, Peru, Based on Integrated Close-Range Remote Sensing and Near-Surface Geophysics
by Ivan Ghezzi, Jacek Kościuk, Warren Church, Parker VanValkenburgh, Bartłomiej Ćmielewski, Matthias Kucera, Paweł B. Dąbek, Jeff Contreras, Nilsson Mori, Giovanni Righetti, Stefano Serafini and Carol Rojas
Remote Sens. 2024, 16(6), 1053; https://doi.org/10.3390/rs16061053 - 16 Mar 2024
Viewed by 2026
Abstract
We combined datasets from multiple research projects and remote sensing technologies to evaluate conservation conditions at La Fortaleza de Kuelap, a pre-Hispanic site in Peru that suffered significant damage under heavy seasonal rains in April 2022. To identify the causes of the collapse [...] Read more.
We combined datasets from multiple research projects and remote sensing technologies to evaluate conservation conditions at La Fortaleza de Kuelap, a pre-Hispanic site in Peru that suffered significant damage under heavy seasonal rains in April 2022. To identify the causes of the collapse and where the monument is at further risk, we modeled surface hydrology using a DTM derived from drone LiDAR data, reconstructed a history of collapses, and calculated the volume of the most recent by fusing terrestrial LiDAR and photogrammetric datasets. In addition, we examined subsurface water accumulation with electrical resistivity, reconstructed the stratification of the monument with seismic refraction, and analyzed vegetation loss and ground moisture accumulation using satellite imagery. Our results point to rainwater infiltration as the most significant source of risk for La Fortaleza’s perimeter walls. Combined with other adverse natural conditions and contemporary conservation interventions, this led to the 2022 collapse. Specialists need to consider these factors when tasked with conserving monuments located in comparable high-altitude perhumid environments. This integration of analytical results demonstrates how multi-scalar and multi-instrumental approaches provide comprehensive and timely assessments of conservation needs. Full article
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21 pages, 14716 KiB  
Article
Toward a Data Fusion Index for the Assessment and Enhancement of 3D Multimodal Reconstruction of Built Cultural Heritage
by Anthony Pamart, Violette Abergel, Livio de Luca and Philippe Veron
Remote Sens. 2023, 15(9), 2408; https://doi.org/10.3390/rs15092408 - 4 May 2023
Cited by 4 | Viewed by 2090
Abstract
In the field of digital cultural heritage (DCH), 2D/3D digitization strategies are becoming more and more complex. The emerging trend of multimodal imaging (i.e., data acquisition campaigns aiming to put in cooperation multi-sensor, multi-scale, multi-band and/or multi-epochs concurrently) implies several challenges in term [...] Read more.
In the field of digital cultural heritage (DCH), 2D/3D digitization strategies are becoming more and more complex. The emerging trend of multimodal imaging (i.e., data acquisition campaigns aiming to put in cooperation multi-sensor, multi-scale, multi-band and/or multi-epochs concurrently) implies several challenges in term of data provenance, data fusion and data analysis. Making the assumption that the current usability of multi-source 3D models could be more meaningful than millions of aggregated points, this work explores a “reduce to understand” approach to increase the interpretative value of multimodal point clouds. Starting from several years of accumulated digitizations on a single use-case, we define a method based on density estimation to compute a Multimodal Enhancement Fusion Index (MEFI) revealing the intricate modality layers behind the 3D coordinates. Seamlessly stored into point cloud attributes, MEFI is able to be expressed as a heat-map if the underlying data are rather isolated and sparse or redundant and dense. Beyond the colour-coded quantitative features, a semantic layer is added to provide qualitative information from the data sources. Based on a versatile descriptive metadata schema (MEMoS), the 3D model resulting from the data fusion could therefore be semantically enriched by incorporating all the information concerning its digitization history. A customized 3D viewer is presented to explore this enhanced multimodal representation as a starting point for further 3D-based investigations. Full article
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23 pages, 12244 KiB  
Article
UAV LiDAR Based Approach for the Detection and Interpretation of Archaeological Micro Topography under Canopy—The Rediscovery of Perticara (Basilicata, Italy)
by Nicola Masini, Nicodemo Abate, Fabrizio Terenzio Gizzi, Valentino Vitale, Antonio Minervino Amodio, Maria Sileo, Marilisa Biscione, Rosa Lasaponara, Mario Bentivenga and Francesco Cavalcante
Remote Sens. 2022, 14(23), 6074; https://doi.org/10.3390/rs14236074 - 30 Nov 2022
Cited by 9 | Viewed by 3059
Abstract
This paper deals with a UAV LiDAR methodological approach for the identification and extraction of archaeological features under canopy in hilly Mediterranean environments, characterized by complex topography and strong erosion. The presence of trees and undergrowth makes the reconnaissance of archaeological features and [...] Read more.
This paper deals with a UAV LiDAR methodological approach for the identification and extraction of archaeological features under canopy in hilly Mediterranean environments, characterized by complex topography and strong erosion. The presence of trees and undergrowth makes the reconnaissance of archaeological features and remains very difficult, while the erosion, increased by slope, tends to adversely affect the microtopographical features of potential archaeological interest, thus making them hardly identifiable. For the purpose of our investigations, a UAV LiDAR survey has been carried out at Perticara (located in Basilicata southern Italy), an abandoned medieval village located in a geologically fragile area, characterized by complex topography, strong erosion, and a dense forest cover. All of these characteristics pose serious challenge issues and make this site particularly significant and attractive for the setting and testing of an optimal LiDAR-based approach to analyze hilly forested regions searching for subtle archaeological features. The LiDAR based investigations were based on three steps: (i) field data acquisition and data pre-processing, (ii) data post-processing, and (iii) semi-automatic feature extraction method based on machine learning and local statistics. The results obtained from the LiDAR based analyses (successfully confirmed by the field survey) made it possible to identify the lost medieval village that represents an emblematic case of settlement abandoned during the crisis of the late Middle Ages that affected most regions in southern Italy. Full article
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