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GIS and RS for Spatial Documentation, Analysis and Interpretation in Multi-Scale Archaeological Applications

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2836

Special Issue Editors

Prof. Dr. Jie He

E-Mail Website
Guest Editor
School of Architecture, Harbin Institute of Technology, Shenzhen, Shenzhen518055, China
Interests: digital humanities; digital archaeology and digital heritage; landscape archaeology; RS archaeology
Special Issues, Collections and Topics in MDPI journals
Dr. Fulong Chen

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, International Centre on Space Technologies for Natural and Cultural Heritage under the Auspices of UNESCO, No. 9, Dengzhuang South Road, Haidian District, Beijing 100094, China
Interests: remote sensing for archaeology; risk mapping and sustainable assessment of monuments and archaeo-landscapes; geoarchaeology; interferometric synthetic aperture radar (InSAR); multi-temporal InSAR (MT-InSAR); change detection and time series analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Massimiliano Pepe

E-Mail Website
Guest Editor
Department of Engineering and Geology (InGeo), “G. d’Annunzio” University of Chieti-Pescara, Viale Pindaro, 42, 65127 Pescara, Italy
Interests: photogrammetry; terrestrial laser scanner; remote sensing; geographic information system; UAV; cultural heritage; heritage; building information modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The role of GIS and Remote Sensing has evolved from providing technical support to embodying foundational methodological presuppositions within archaeology's "spatial turn" and data-driven paradigms.

This special issue explicitly focuses on applying these spatial approaches to address archaeological problems across diverse scales and contexts. The term "space" encompasses objects and phenomena across multiple scales, ranging from macro-level perspectives—such as global, maritime, and regional—to medium-scale entities like landscapes, cities, and sites, and down to micro-scale elements, including the distribution of traces, artifacts, and ecofacts. It includes spatial dimensions across two-dimensional (2D), three-dimensional (3D), and temporal frameworks.

We invite contributions utilizing spatial information technologies for the documentation, analysis, interpretation, and narrative construction of archaeological spaces. This issue aims to advance spatial methodologies in archaeological inquiry by showcasing concrete applications of remote sensing and spatial data science. We particularly encourage submissions discussing innovative spatial data acquisition technologies (e.g. LiDAR, Unmanned Aerial Vehicle (UAV), Autonomous Underwater Vehicle (AUV) and underwater photogrammetry), Geospatial Artificial Intelligence (GeoAI), Large Language Models (LLMs), spatial big data, spatial knowledge graph, immersive digital environments and interactive spatial visualization, and other emerging spatial information technologies.

Prof. Dr. Jie He
Prof. Dr. Fulong Chen
Dr. Massimiliano Pepe
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 250 words) can be sent to the Editorial Office for assessment.

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

  • GIS
  • remote sensing
  • archaeological space
  • archaeological application
  • multiple scales
  • archaeological documentation
  • spatial analysis
  • spatial interpretation and narrative

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

Published Papers (3 papers)

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Research

38 pages, 22393 KB  
Article
High-Resolution 3D Structural Documentation of the Saqqara Pyramids, Egypt, Using Terrestrial Laser Scanning and Integrated Geomatics Techniques for Heritage Preservation
by Abdelhamid Elbshbeshi, Abdelmonem Mohamed and Ismael M. Ibraheem
Remote Sens. 2026, 18(8), 1138; https://doi.org/10.3390/rs18081138 - 11 Apr 2026
Viewed by 767
Abstract
Accurate 3D documentation of large and complex structures is essential for long-term stability assessment, structural monitoring, and conservation planning, particularly for heritage sites exposed to environmental and anthropogenic threats. This study develops an integrated workflow combining Terrestrial Laser Scanning (TLS), Global Navigation Satellite [...] Read more.
Accurate 3D documentation of large and complex structures is essential for long-term stability assessment, structural monitoring, and conservation planning, particularly for heritage sites exposed to environmental and anthropogenic threats. This study develops an integrated workflow combining Terrestrial Laser Scanning (TLS), Global Navigation Satellite System (GNSS), and Total Station geodetic control for large-scale, high-precision documentation. The approach was implemented at the Saqqara archaeological zone, a UNESCO World Heritage Site facing significant deterioration risks, to document four major pyramids: Djoser, Unas, Teti, and Userkaf. More than 2.1 billion georeferenced points were acquired from 16 scan positions with sub-centimeter registration errors and overall geometric accuracy better than ±1 cm. From these datasets, detailed mesh models, orthoimages, Digital Elevation Models (DEMs), contour maps, and 2D plans were derived. These enabled quantitative analyses of height loss and volumetric change, indicating severe structural degradation in Unas (~53%), Teti (~66%), and Userkaf (~63%), as well as localized deformations such as 4.2 cm displacement at Teti’s south flank. The degradation results from environmental factors and anthropogenic influences. Beyond this case study, the workflow proves that integrated TLS documentation can be applied to large and complex structures, supporting deformation monitoring, stability assessment, and digital twin development. Full article
(This article belongs to the Special Issue GIS and RS for Spatial Documentation, Analysis and Interpretation in Multi-Scale Archaeological Applications)
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29 pages, 12904 KB  
Article
Evaluating the Impact of Multi-Source Digital Elevation Model Quality on Archeological Predictive Modeling: An Integrated Framework Based on Machine Learning and SHAP-Based Interpretability Analysis
by Jia Yang, Jianghong Zhao, Pengcheng Hao, Aomeng Zhang, Xiaopeng Li, Ran Tu and Zhi Zhang
Remote Sens. 2026, 18(6), 961; https://doi.org/10.3390/rs18060961 - 23 Mar 2026
Viewed by 640
Abstract
Digital Elevation Models (DEMs) constitute a core data source for Archeological Predictive Modeling. However, how quality differences among multi-source DEM propagate through complex models and subsequently affect predictive accuracy and geographic interpretation remains insufficiently understood. This study aims to develop an integrated evaluation [...] Read more.
Digital Elevation Models (DEMs) constitute a core data source for Archeological Predictive Modeling. However, how quality differences among multi-source DEM propagate through complex models and subsequently affect predictive accuracy and geographic interpretation remains insufficiently understood. This study aims to develop an integrated evaluation framework that combines machine learning with SHAP-based interpretability analysis to systematically compare the suitability of mainstream open access DEM products for archeological site prediction. The results indicate that (1) in terms of vertical accuracy, Copernicus DEM and TanDEM-X achieved the best performance, with RMSE values of 2.19 m and 2.31 m, respectively, whereas ASTER exhibited the lowest accuracy (RMSE = 6.44 m) and exaggerated terrain. (2) Regarding model performance, Copernicus DEM-driven models demonstrated the highest robustness, achieving an AUC of 0.966 under the XGBoost algorithm. (3) Interpretability analysis revealed that different DEM products significantly reallocate the importance of key variables such as slope and the Topographic Wetness Index, potentially distorting scientific interpretations of ancient military defensive site-selection patterns. Copernicus DEM is recommended as a priority data source. Moreover, while pursuing higher spatial resolution, equal attention must be paid to vertical accuracy and consistency with geomorphological logic. Full article
(This article belongs to the Special Issue GIS and RS for Spatial Documentation, Analysis and Interpretation in Multi-Scale Archaeological Applications)
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20 pages, 2738 KB  
Article
Remote Sensing Image Super-Resolution for Heritage Sites Using a Temporal Invariance-Aware Training Strategy
by Caiyan Chen, Fulong Chen, Sheng Gao, Hongqiang Li, Xinru Zhang and Yanni Cheng
Remote Sens. 2026, 18(1), 118; https://doi.org/10.3390/rs18010118 - 29 Dec 2025
Viewed by 591
Abstract
Effective spatial and structural monitoring of World Heritage sites often relies on continuous high-spatial-resolution remote sensing imagery, which is often unavailable for specific years due to sensor, atmospheric, and revisit constraints. Super-resolution reconstruction thus becomes crucial for maintaining data continuity for such analyses. [...] Read more.
Effective spatial and structural monitoring of World Heritage sites often relies on continuous high-spatial-resolution remote sensing imagery, which is often unavailable for specific years due to sensor, atmospheric, and revisit constraints. Super-resolution reconstruction thus becomes crucial for maintaining data continuity for such analyses. Traditional methods are trained on temporally aligned LR-HR pairs; however, their performance significantly declines when applied to unseen years due to temporal distribution shifts. To address this, we propose a temporal invariance-aware training strategy combined with an improved Residual Dense Network (RDN_2_M). We introduce a cross-year masked sample generation algorithm that identifies temporally stable regions via local structural similarity. This constructs explicit invariance-guided training pairs, which helps guide the model to focus on persistent structural features rather than transient appearances and to learn robust representations against inter-annual variations. Experiments on the Bin County Cave Temple (BCCT) Heritage Site dataset show our method, integrating the proposed strategy with the enhanced RDN model (RDN_2_M), significantly improves both the objective metrics and visual quality of reconstructed images. This offers a practical solution to filling temporal data gaps, thereby supporting long-term spatial and structural heritage monitoring. Full article
(This article belongs to the Special Issue GIS and RS for Spatial Documentation, Analysis and Interpretation in Multi-Scale Archaeological Applications)
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