Innovative Remote Sensing Approaches: 3D Reconstruction, UAV Photogrammetry, and BIM in Cultural Heritage and Infrastructure

A special issue of Geomatics (ISSN 2673-7418).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1257

Editors


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Guest Editor
Department of Computer Science, University of Jaén, 23071 Jaén, Spain
Interests: photogrammetry; computational geometry; visibility; urban modeling; GIS
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Cartographic, Geodetic and Photogrammetric Engineering Department, University of Jaén, 23071 Jaén, Spain
Interests: precision farming; remote sensing; spatial data mining; geospatial data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The digital transformation of the built environment relies heavily on high-resolution spatial data acquisition. The convergence of unmanned aerial vehicles (UAVs) and advanced sensors has made methods such as 3D reconstruction and photogrammetry indispensable tools, transforming traditional 2D documentation into dynamic and highly dense 3D models and representations. This technological shift is paramount for two critical sectors: cultural heritage (CH), where accurate geometric and material documentation is fundamental for conservation, virtual preservation, and public access; and infrastructure, where precise monitoring and modeling are vital for safety, lifecycle management, and resilience against environmental factors. Addressing the challenge of extracting meaningful information and semantic intelligence from raw point cloud data represents the frontier of modern geomatics and applied engineering.

This Special Issue is dedicated to showcasing cutting-edge research that bridges the gap between raw spatial data acquisition and advanced semantic modeling frameworks. We seek contributions that move beyond simple visualization, focusing on the integration of data acquisition techniques with information management systems, primarily building information modeling (BIM), and its heritage-specific adaptation, HBIM (Heritage BIM). By prioritizing technological innovations and the development of integrated workflows for documentation, monitoring, and analysis, this Special Issue directly supports and expands the journal's scope in publishing applied engineering, computer vision, and geospatial solutions for the complex challenges of the modern built world.

We invite original contributions that explore novel methodologies, algorithms, and applications related to the Special Issue’s core topics. Suggested themes include, but are not limited to, the following: (1) BIM-based modeling from 3D point clouds for existing structures (Scan-to-BIM/HBIM); (2) development of automated feature extraction and semantic enrichment algorithms; (3) data fusion techniques and advanced data models for managing heterogeneous information (e.g., combining LiDAR, UAV photogrammetry); (4) remote sensing for damage detection, deformation analysis, and structural health monitoring (SHM) of civil infrastructure; and (5) novel visualization methods and immersive technologies (AR/VR) for documented assets.

We welcome the submission of original research articles, comprehensive review articles, and detailed case studies that demonstrate the implementation and validation of these innovative approaches.

Dr. Lidia M. Ortega Alvarado
Dr. María I. Ramos Galan
Guest Editors

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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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Geomatics 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 1200 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

  • UAV photogrammetry
  • scan-to-BIM
  • cultural heritage management
  • structural health monitoring (SHM)
  • semantic modeling
  • point cloud processing

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Published Papers (2 papers)

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Research

19 pages, 35417 KB  
Article
Deep Learning and Multiview-Based Detection of Scatterable PFM-1 Landmines: Performance, Out-of-Sample Evaluation, and Field Readiness
by Sharifa Karwandyar, Thomas J. Pingel and Alex Nikulin
Geomatics 2026, 6(3), 54; https://doi.org/10.3390/geomatics6030054 - 19 May 2026
Viewed by 435
Abstract
The detection and classification of scatterable landmines present a significant challenge for humanitarian demining, particularly in resource-constrained regions. This paper evaluates the use of a deep learning-based strategy using RGB imagery and the YOLOv11 algorithm to detect the most commonly deployed PFM-1 landmines, [...] Read more.
The detection and classification of scatterable landmines present a significant challenge for humanitarian demining, particularly in resource-constrained regions. This paper evaluates the use of a deep learning-based strategy using RGB imagery and the YOLOv11 algorithm to detect the most commonly deployed PFM-1 landmines, with the overarching goal of applying this approach to the broad category of scatterable landmines. RGB image-based YOLOv11 detection showed strong precision (78–91%) and recall (76–88%) against validation data for several model variants. Additionally, 3D-printed, paint-matched replicas of PFM-1 landmines were used provisionally as part of out-of-sample (OOS) testing to assess the realistic value of this methodology in the field, along with an inert PFM-1 mine. This demonstrated the potential for 3D-printed replicas to be used as part of the training and assessment process due to their low-cost, scalable, and safe approach, highlighting strong precision (74–80%) but weaker recall (14–24%). Additional edge deployment was tested using the model to demonstrate its capability in locating a minefield using trigonometric relationships and kernel density relationships, further supporting this method in non-technical, first-pass landmine sweeps. These results demonstrate that OOS evaluation is critical in humanitarian demining research to ensure that detection systems are truly field-ready and operationally reliable. This study provides a replicable workflow for deep learning tasks related to surface-laid landmines that can be deployed on edge devices for use in non-technical surveys. Full article
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18 pages, 7891 KB  
Article
Evaluation of the Accuracy of Direct Georeferencing of Photogrammetric Products in a Large Area with Steep Topography
by Dania Isaura Pasillas-Pasillas, Juvenal Villanueva-Maldonado, Carlos Bautista-Capetillo, José Ricardo Gómez Rodríguez, Erick Dante Mattos-Villarroel and Cruz Octavio Robles Rovelo
Geomatics 2026, 6(3), 52; https://doi.org/10.3390/geomatics6030052 - 15 May 2026
Viewed by 276
Abstract
Technological advancements have revolutionized photogrammetry, with the implementation of unmanned aerial vehicles for capturing images from different angles and the ease of obtaining sensor position information at the time of capture. This study evaluates the accuracy of direct georeferencing via Networked Transport of [...] Read more.
Technological advancements have revolutionized photogrammetry, with the implementation of unmanned aerial vehicles for capturing images from different angles and the ease of obtaining sensor position information at the time of capture. This study evaluates the accuracy of direct georeferencing via Networked Transport of Radio Technical Commission for Maritime Services Via Internet Protocol, in the orthomosaic as a photogrammetric product in a large urban area with steep and highly variable topography, comparing it with the coordinates of nine checkpoints obtained with GNSS equipment connected to the National Active Geodetic Network, managed by the National Institute of Statistics and Geography of Mexico. An orthomosaic of the historic center of Zacatecas was obtained with a resolution of 2.70 cm/pixel. The orthomosaic coordinates, compared to those of the GNSS equipment, show a root mean square error (RMSE) of 0.78 m in the horizontal coordinates and an RMSE of 1.22 m in the vertical coordinates. Previous studies prove the efficiency of the Continuously Operating Reference Station module and network with other aircraft; this study determines that this is true for large areas with high coverage and quality in the internet network, but with rugged topography, the results are not accurate. Full article
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