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Remote Sensing, Archaeology and Heritage Research: Researching the Past from Satellite, Aerial and Terrestial Methods

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 34687

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Special Issue Editors

Dr. Antonio Monterroso Checa

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Guest Editor

Patricia. Unit for R&D in Cultural Heritage (HUM 882 Archaeological Research Group), Universidad de Córdoba, Campus de Rabanales, Edificio C1, Carretera Nacional IV, km 396, 14014 Córdoba, Spain
Interests: remote sensing; cultural heritage; landscape archaeology; archaeological prospection; 3D dissemination

Dr. Massimo Gasparini

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Guest Editor

Patricia. Unit for R&D in Cultural Heritage (HUM 882 Archaeological Research Group), Universidad de Córdoba, Campus de Rabanales, Edificio C1, Carretera Nacional IV, km 396, 14014 Córdoba, Spain
Interests: remote sensing; photogrammetry; geophysical prospection; terrestrial laser scanning; geographic information system; landscape archaeology; cultural heritage

Special Issue Information

Dear Colleagues,

This Special Issue “Satellite and Ground-Based Remote Sensing for Archaeological and Heritage Research” invites researchers utilizing remote sensing to develop high-quality archaeological projects or interventions to submit articles. The main aims and scope are to collect articles on strong archaeological and historical heritage findings utilizing remote sensing material, strategies, and methods.

This Special Issue seeks papers approaching the topic from different perspectives and interdisciplinary proposals, which includes the consideration of different areas of archaeological and heritage knowledge related to SSH (e.g., archaeological excavations and prospections, landscape archaeology, conservation research, architecture). Articles utilizing remote sensing methods to virtualize excavations, monuments or landscapes and articles improving rural landscapes through archaeological and heritage research are especially welcome.

Review articles covering one or more of these topics are also welcome.

Dr. Antonio Monterroso Checa
Dr. Massimo Gasparini
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

Ground-based geophysics Aerial and satellite imagery
LiDAR and laser scanning
SAR
Photogrammetric acquisitions
Deep learning-based automated detection of archaeological sites
Geographic Information System

Published Papers (10 papers)

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Research

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29 pages, 60974 KiB

Open AccessArticle

Appraisal of Ancient Quarries and WWII Air Raids as Factors of Subsidence in Rome: A Geomatic Approach

by Angela Celauro, José Antonio Palenzuela Baena, Ilaria Moriero, Alexander Maass, José Francisco Guerrero Tello, Peppe Junior Valentino D’Aranno and Maria Marsella

Remote Sens. 2023, 15(8), 2011; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15082011 - 11 Apr 2023

Viewed by 1166

Abstract

Ancient mining and quarrying activities left anthropogenic geomorphologies that have shaped the natural landscape and affected environmental equilibria. The artificial structures and their related effects on the surrounding environment are analyzed here to characterize the quarrying landscape in the southeast area of Rome in terms of its dimensions, typology, state of preservation and interface with the urban environment. The increased occurrence of sinkhole events in urban areas has already been scientifically correlated to ancient cavities under increasing urban pressure. In this scenario, additional interacting anthropogenic factors, such as the aerial bombardments perpetrated during the Second World War, are considered here. These three factors have been investigated by employing a combined geomatic methodology. Information on air raids has been organized in vector archives. A dataset of historical aerial photographs has been processed into Digital Surface Models and orthomosaics to reconstruct the quarry landscape and its evolution, identify typologies of exploitation and forms of collapse and corroborate the discussion concerning the induced historical and recent subsidence phenomena, comparing these outputs with photogrammetric products obtained from recent satellite data. Geological and urbanistic characterization of the study area allowed a better connection between these historical and environmental factors. In light of the information gathered so far, SAR interferometric products allowed a preliminary interpretation of ground instabilities surrounding historical quarries, air raids and recent subsidence events. Various sub-areas of the AOI where the presence of the considered factors also corresponds to areas in slight subsidence in the SAR velocity maps have been highlighted. Bivariate hotspot analysis allowed substantiating the hypothesis of a spatial correlation between these multiple aspects. Full article

(This article belongs to the Special Issue Remote Sensing, Archaeology and Heritage Research: Researching the Past from Satellite, Aerial and Terrestial Methods)

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27 pages, 5356 KiB

Open AccessArticle

Combining Remote Sensing Approaches for Detecting Marks of Archaeological and Demolished Constructions in Cahokia’s Grand Plaza, Southwestern Illinois

by Israa Kadhim, Fanar M. Abed, Justin M. Vilbig, Vasit Sagan and Caitlin DeSilvey

Remote Sens. 2023, 15(4), 1057; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15041057 - 15 Feb 2023

Cited by 3 | Viewed by 1906

Abstract

Remote sensing data are increasingly being used in digital archaeology for the potential non-invasive detection of archaeological remains. The purpose of this research is to evaluate the capability of standalone (LiDAR and aerial photogrammetry) and integration/fusion remote sensing approaches in improving the prospecting and interpretation of archaeological remains in Cahokia’s Grand Plaza. Cahokia Mounds is an ancient area; it was the largest settlement of the Mississippian culture located in southwestern Illinois, USA. There are a limited number of studies combining LiDAR and aerial photogrammetry to extract archaeological features. This article, therefore, combines LiDAR with photogrammetric data to create new datasets and investigate whether the new data can enhance the detection of archaeological/ demolished structures in comparison to the standalone approaches. The investigations are implemented based on the hillshade, gradient, and sky view factor visual analysis techniques, which have various merits in revealing topographic features. The outcomes of this research illustrate that combining data derived from different sources can not only confirm the detection of remains but can also reveal more remains than standalone approaches. This study demonstrates that the use of combination remote sensing approaches provides archaeologists with another powerful tool for site analysis. Full article

(This article belongs to the Special Issue Remote Sensing, Archaeology and Heritage Research: Researching the Past from Satellite, Aerial and Terrestial Methods)

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21 pages, 6908 KiB

Open AccessArticle

Discovering the Ancient Tomb under the Forest Using Machine Learning with Timing-Series Features of Sentinel Images: Taking Baling Mountain in Jingzhou as an Example

by Yichuan Liu, Qingwu Hu, Shaohua Wang, Fengli Zou, Mingyao Ai and Pengcheng Zhao

Remote Sens. 2023, 15(3), 554; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030554 - 17 Jan 2023

Cited by 3 | Viewed by 2544

Abstract

Cultural traces under forests are one of the main problems affecting the identification of archaeological sites in densely forested areas, so it is full of challenges to discover ancient tombs buried under dense vegetation. The covered ancient tombs can be identified by studying the time-series features of the vegetation covering the ancient tombs on the multi-time series remote sensing images because the ancient tombs buried deep underground have long-term underground space structures, which affect the intrinsic properties of the surface soil so that the growth status of the covering vegetation is different from that of the vegetation in the area without ancient tombs. We first use the highly detailed DSM data to select the ancient tombs that cannot be visually distinguished on the optical images. Then, we explored and constructed the temporal features of the ancient tombs under the forest and the non-ancient tombs in the images, such as the radar timing-series features of Sentinel 1 and the multi-spectral and vegetation index timing-series features of Sentinel 2. Finally, based on these features and machine learning, we designed an automatic identification algorithm for ancient tombs under the forest. The method has been validated in Baling Mountain in Jingzhou, China. It is very feasible to automatically identify ancient tombs covered by surface vegetation by using the timing-series features of remote sensing images. Additionally, the identification of large ancient tombs or concentrated ancient tombs is more accurate, and the accuracy is improved after adding radar features. The paper concludes with a discussion of the current limitations and future directions of the method. Full article

(This article belongs to the Special Issue Remote Sensing, Archaeology and Heritage Research: Researching the Past from Satellite, Aerial and Terrestial Methods)

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18 pages, 6022 KiB

Open AccessArticle

Observations of Archaeological Proxies through Phenological Analysis over the Megafort of Csanádpalota-Juhász T. tanya in Hungary Using Sentinel-2 Images

by Athos Agapiou, Alexandru Hegyi and Andrei Stavilă

Remote Sens. 2023, 15(2), 464; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020464 - 12 Jan 2023

Cited by 3 | Viewed by 1348

Abstract

This study aims to investigate potential archaeological proxies at a large Bronze Age fortification in Hungary, namely the Csanádpalota–Juhász T. tanya site, using open-access satellite data. Available Sentinel-2 images acquired between April 2017 and September 2022 were used. More than 700 images (727) were initially processed and filtered, accounting at the end of more than 400 (412) available calibrated Level 2A Sentinel images over the case study area. Sentinel-2 images were processed through image analysis. Based on pan-sharpened data, the visibility of crop marks was improved and enhanced by implementing orthogonal equations. Several crop marks, some still unknown, were revealed in this study. In addition, multi-temporal phenological observations were recorded on three archaeological proxies (crop marks) within the case study area, while an additional area was selected for calibration purposes (agricultural field). Phenological observations were performed for at least four complete phenological cycles throughout the study period. Statistical comparisons between the selected archaeological proxies were applied using a range of vegetation indices. The overall results indicated that phenological observations could be used as archaeological proxies for detecting the formation of crop marks. Full article

(This article belongs to the Special Issue Remote Sensing, Archaeology and Heritage Research: Researching the Past from Satellite, Aerial and Terrestial Methods)

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34 pages, 14399 KiB

Open AccessArticle

Multiclass Land Cover Mapping from Historical Orthophotos Using Domain Adaptation and Spatio-Temporal Transfer Learning

by Wouter A. J. Van den Broeck, Toon Goedemé and Maarten Loopmans

Remote Sens. 2022, 14(23), 5911; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14235911 - 22 Nov 2022

Cited by 1 | Viewed by 1887

Abstract

Historical land cover (LC) maps are an essential instrument for studying long-term spatio-temporal changes of the landscape. However, manual labelling on low-quality monochromatic historical orthophotos for semantic segmentation (pixel-level classification) is particularly challenging and time consuming. Therefore, this paper proposes a methodology for the automated extraction of very-high-resolution (VHR) multi-class LC maps from historical orthophotos under the absence of target-specific ground truth annotations. The methodology builds on recent evolutions in deep learning, leveraging domain adaptation and transfer learning. First, an unpaired image-to-image (I2I) translation between a source domain (recent RGB image of high quality, annotations available) and the target domain (historical monochromatic image of low quality, no annotations available) is learned using a conditional generative adversarial network (GAN). Second, a state-of-the-art fully convolutional network (FCN) for semantic segmentation is pre-trained on a large annotated RGB earth observation (EO) dataset that is converted to the target domain using the I2I function. Third, the FCN is fine-tuned using self-annotated data on a recent RGB orthophoto of the study area under consideration, after conversion using again the I2I function. The methodology is tested on a new custom dataset: the ‘Sagalassos historical land cover dataset’, which consists of three historical monochromatic orthophotos (1971, 1981, 1992) and one recent RGB orthophoto (2015) of VHR (0.3–0.84 m GSD) all capturing the same greater area around Sagalassos archaeological site (Turkey), and corresponding manually created annotations (2.7 km² per orthophoto) distinguishing 14 different LC classes. Furthermore, a comprehensive overview of open-source annotated EO datasets for multiclass semantic segmentation is provided, based on which an appropriate pretraining dataset can be selected. Results indicate that the proposed methodology is effective, increasing the mean intersection over union by 27.2% when using domain adaptation, and by 13.0% when using domain pretraining, and that transferring weights from a model pretrained on a dataset closer to the target domain is preferred. Full article

(This article belongs to the Special Issue Remote Sensing, Archaeology and Heritage Research: Researching the Past from Satellite, Aerial and Terrestial Methods)

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18 pages, 14416 KiB

Open AccessArticle

Deep Learning for Archaeological Object Detection on LiDAR: New Evaluation Measures and Insights

by Marco Fiorucci, Wouter B. Verschoof-van der Vaart, Paolo Soleni, Bertrand Le Saux and Arianna Traviglia

Remote Sens. 2022, 14(7), 1694; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071694 - 31 Mar 2022

Cited by 13 | Viewed by 5269

Abstract

Machine Learning-based workflows are being progressively used for the automatic detection of archaeological objects (intended as below-surface sites) in remote sensing data. Despite promising results in the detection phase, there is still a lack of a standard set of measures to evaluate the performance of object detection methods, since buried archaeological sites often have distinctive shapes that set them aside from other types of objects included in mainstream remote sensing datasets (e.g., Dataset of Object deTection in Aerial images, DOTA). Additionally, archaeological research relies heavily on geospatial information when validating the output of an object detection procedure, a type of information that is not normally considered in regular machine learning validation pipelines. This paper tackles these shortcomings by introducing two novel automatic evaluation measures, namely ‘centroid-based’ and ‘pixel-based’, designed to encode the salient aspects of the archaeologists’ thinking process. To test their usability, an experiment with different object detection deep neural networks was conducted on a LiDAR dataset. The experimental results show that these two automatic measures closely resemble the semi-automatic one currently used by archaeologists and therefore can be adopted as fully automatic evaluation measures in archaeological remote sensing detection. Adoption will facilitate cross-study comparisons and close collaboration between machine learning and archaeological researchers, which in turn will encourage the development of novel human-centred archaeological object detection tools. Full article

(This article belongs to the Special Issue Remote Sensing, Archaeology and Heritage Research: Researching the Past from Satellite, Aerial and Terrestial Methods)

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14 pages, 4614 KiB

Open AccessArticle

Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms, and Deep Learning

by Dylan S. Davis and Julius Lundin

Remote Sens. 2021, 13(18), 3680; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183680 - 15 Sep 2021

Cited by 11 | Viewed by 3792

Abstract

Over the past several centuries, the iron industry played a central role in the economy of Sweden and much of northern Europe. A crucial component of iron manufacturing was the production of charcoal, which was often created in charcoal piles. These features are visible in LiDAR (light detection and ranging) datasets. These charcoal piles vary in their morphology by region, and training data for some feature types are severely lacking. Here, we investigate the potential for machine automation to aid archaeologists in recording charcoal piles with limited training data availability in a forested region of Jönköping County, Sweden. We first use hydrological depression algorithms to conduct a preliminary assessment of the study region and compile suitable training data for charcoal production sites. Then, we use these datasets to train a series of RetinaNet deep learning models, which are less computationally expensive than many popular deep learning architectures (e.g., R-CNNs), allowing for greater usability. Together, our results demonstrate how charcoal piles can be automatically extracted from LiDAR datasets, which has great implications for improving our understanding of the long-term environmental impact of the iron industry across Northern Europe. Furthermore, our workflow for developing and implementing deep learning models for archaeological research can expand the use of such methods to regions that lack suitable training data. Full article

(This article belongs to the Special Issue Remote Sensing, Archaeology and Heritage Research: Researching the Past from Satellite, Aerial and Terrestial Methods)

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28 pages, 10832 KiB

Open AccessArticle

Lost Landscape: A Combination of LiDAR and APSFR Data to Locate and Contextualize Archaeological Sites in River Environments

by Esther Rodríguez González, Pablo Paniego Díaz and Sebastián Celestino Pérez

Remote Sens. 2021, 13(17), 3506; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173506 - 03 Sep 2021

Cited by 5 | Viewed by 3387

Abstract

Over the last few decades, river landscapes have been significantly transformed as a result of increased human impact. This transformation is evident in areas such as the middle Guadiana basin, where the impact of both agricultural and hydraulic infrastructures has led to the decontextualization of archaeological sites, resulting in a disconnection between archaeological sites and their own physical environment. In order to analyse the location and geographic contexts of sites from the first Iron Age in the middle Guadiana basin and to detect the existence of human settlement patterns, we designed a methodological approach that combines LiDAR and APSFR data (areas with potential significant flood risk). The main purpose of this method is to detect flood areas and assess the relationship between them and archaeological sites. The result allowed us to obtain a clearer understanding of these societies, their knowledge of the physical environment, and the causes and reasons behind their occupation of certain sites. The validation of the results demonstrated the versatility of this methodological approach, which can be extrapolated to analysing other regions and historical periods. Full article

(This article belongs to the Special Issue Remote Sensing, Archaeology and Heritage Research: Researching the Past from Satellite, Aerial and Terrestial Methods)

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30 pages, 21524 KiB

Open AccessArticle

An Integrated Remote-Sensing and GIS Approach for Mapping Past Tin Mining Landscapes in Northwest Iberia

by João Fonte, Emmanuelle Meunier, José Alberto Gonçalves, Filipa Dias, Alexandre Lima, Luís Gonçalves-Seco and Elin Figueiredo

Remote Sens. 2021, 13(17), 3434; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173434 - 29 Aug 2021

Cited by 8 | Viewed by 3672

Abstract

Northwest Iberia can be considered as one of the main areas where tin was exploited in antiquity. However, the location of ancient tin mining and metallurgy, their date and the intensity of tin production are still largely uncertain. The scale of mining activity and its socio-economical context have not been truly assessed, nor its evolution over time. With the present study, we intend to present an integrated, multiscale, multisensor and interdisciplinary methodology to tackle this problem. The integration of airborne LiDAR and historic aerial imagery has enabled us to identify and map ancient tin mining remains on the Tinto valley (Viana do Castelo, northern Portugal). The combination with historic mining documentation and literature review allowed us to confirm the impact of modern mining and define the best-preserved ancient mining areas for further archaeological research. After data processing and mapping, subsequent ground-truthing involved field survey and geological sampling that confirmed cassiterite exploitation as the key feature of the mining works. This non-invasive approach is of importance for informing future research and management of these landscapes. Full article

(This article belongs to the Special Issue Remote Sensing, Archaeology and Heritage Research: Researching the Past from Satellite, Aerial and Terrestial Methods)

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Review

Jump to: Research

23 pages, 4581 KiB

Open AccessReview

A Review of Artificial Intelligence and Remote Sensing for Archaeological Research

by Argyro Argyrou and Athos Agapiou

Remote Sens. 2022, 14(23), 6000; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14236000 - 26 Nov 2022

Cited by 18 | Viewed by 5276

Abstract

The documentation and protection of archaeological and cultural heritage (ACH) using remote sensing, a non-destructive tool, is increasingly popular for experts around the world, as it allows rapid searching and mapping at multiple scales, rapid analysis of multi-source data sets, and dynamic monitoring of ACH sites and their environments. The exploitation of remote sensing data and their products have seen an increased use in recent years in the fields of archaeological science and cultural heritage. Different spatial and spectral analysis datasets have been applied to distinguish archaeological remains and detect changes in the landscape over time, and, in the last decade, archaeologists have adopted more thoroughly automated object detection approaches for potential sites. These approaches included, among others, object detection methods, such as those of machine learning (ML) and deep learning (DL) algorithms, as well as convolutional neural networks (CNN) and deep learning (DL) models using aerial and satellite images, airborne and spaceborne remote sensing (ASRS), multispectral, hyperspectral images, and active methods (synthetic aperture radar (SAR) and light detection and ranging radar (LiDAR)). Researchers also refer to the potential for archaeologists to explore such artificial intelligence (AI) approaches in various ways, such as identifying archaeological features and classifying them. Here, we present a review study related to the contributions of remote sensing (RS) and artificial intelligence in archaeology. However, a main question remains open in the field of research: the rate of positive contribution of remote sensing and artificial intelligence techniques in archaeological research. The scope of this study is to summarize the state of the art related to AI and RS for archaeological research and provide some further insights into the existing literature. Full article

(This article belongs to the Special Issue Remote Sensing, Archaeology and Heritage Research: Researching the Past from Satellite, Aerial and Terrestial Methods)

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