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Special Issue "Technologies for Heritage Knowledge and Preservation: 3D Point Cloud Modelling, GIS, HBIM, Simulation, Immersive Experiences"

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

Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 5105

Special Issue Editors

1. Departamento de Expresión Gráfica e Ingeniería en la Edificación, Escuela Técnica Superior de Ingeniería de Edificación, Universidad de Sevilla, 4A Reina Mercedes Avenue, 41012 Seville, Spain
2. Product Innovation Centre & The Creative and Virtual Technologies Research Lab, School of Architecture, Design and the Built Environment, Nottingham Trent University, Nottingham NG1 4FQ, UK
Interests: 3D scanning; as-built 3D modelling; HBIM; model accuracy; point cloud data; infrared thermography
1. Instituto Geográfico Nacional (National Geographic Institute) of Spain, Andalusian Division, 41013 Seville, Spain
2. Department of Graphic Engineering, University of Seville, 41012 Seville, Spain
Interests: geomatics; GIS; geosciences; natural hazards; seismology; engineering education; remote sensing
Special Issues, Collections and Topics in MDPI journals
Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, 70125 Bari, Italy
Interests: building diagnostics; digital survey; historic building information modelling; virtual reality
Special Issues, Collections and Topics in MDPI journals
Department of Electrical Engineering, Computer Engineering and Informatics, Faculty of Engineering and Technology, Cyprus University of Technology, Limassol 3036, Cyprus
Interests: digital heritage; 3D reconstruction; 3D modelling; ΒΙΜ; ΗΒΙΜ; standardisation in cultural heritage; holistic documentation in cultural heritage; XR

Special Issue Information

Dear Colleagues,

With the advent of remote sensing technologies, the study of architectural, archaeological and cultural heritage has witnessed a considerable reduction in the time needed to record, process and represent data compared to traditional methods. This includes the use of geometric data-capture technologies, such as Terrestrial Laser Scanning (TLS) and the Structure-from-Motion (SfM) photogrammetric technique, and Unmanned Aerial Vehicles (UAVs) to reach inaccessible areas or provide a general view. Focusing on geometry, scientific research has addressed point cloud data classification and segmentation using artificial intelligence. Besides, the as-is/as-built 3D parametric modelling from the massive data capture technologies mentioned above makes it possible to represent the geometrical alterations in the assets, which impacts the accuracy of potential analyses. Historic Building Information Modelling (HBIM), the application of BIM technology to heritage, follows the parametric modelling process associated with relevant information on the heritage asset.

Another benefit of remote sensing technologies is that they do not require contact with the bodies, thus allowing for building diagnostics through non-destructive testing (NDT) and simulation, as in the case of infrared thermography. Additionally, Geographic Information Systems (GIS) are useful to integrate, manage, analyse, and represent heritage-related data.

Finally, interactive technologies focused on users’ experiences such as Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) allow for immersive content to understand, explore, and disseminate heritage.

However, despite all these advances, further research is required into digital technologies applied to the heritage field to contribute to its understanding and preservation over time. New applications, combinations of software and technologies, processes, and innovative technologies are needed to ensure a greater degree of automation, simplicity of workflows, and more realistic immersive experiences. There is particularly significant room for improvement in the point cloud semantic segmentation and feature recognition for the Scan-to-HBIM process, building diagnostics from remote sensing and as-is geometry-based simulation, and the application of the latest game-engine rendering capabilities.

This Special Issue aims to gather original contributions on the use of digitisation, visualisation, management, analysis, and exploration technologies to support the understanding, study, conservation, and dissemination of heritage assets.

Research articles, review articles, and case studies are welcome. Papers may address, but are not limited to, the following topics:

  • Advances in 3D modelling approaches from 3D point cloud data.
  • Automation of the Scan-to-HBIM process.
  • Use of GIS and HBIM for heritage management and analysis.
  • 3D point cloud data semantic segmentation and feature recognition.
  • Sensor data fusion.
  • Simulation and non-destructive testing for heritage conservation.
  • Game-engine rendering immersive experiences of heritage assets.

Dr. Daniel Antón
Dr. José L. Amaro-Mellado
Dr. Silvana Bruno
Dr. Marinos Ioannides
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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.


  • 3D point cloud modelling
  • historic building information modelling
  • geographic information system
  • terrestrial laser scanning
  • unmanned aerial vehicle
  • structure-from-motion
  • semantic segmentation
  • data fusion
  • simulation and non-destructive testing
  • game engine

Published Papers (1 paper)

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24 pages, 17595 KiB  
Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction
Remote Sens. 2023, 15(13), 3352; - 30 Jun 2023
Viewed by 886
The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we propose a framework for historical city [...] Read more.
The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we propose a framework for historical city reconstruction. By integrating procedural modeling techniques and machine learning models within a Geographic Information System (GIS) framework, our pipeline allows for effective management of spatial data and the generation of detailed 3D models. We developed an open-source Python module that fills gaps in 2D GIS datasets and directly generates 3D models up to LOD 2.1 from GIS files. The use of the CityJSON format ensures interoperability and accommodates the specific needs of historical models. A practical case study using footprints of the Old City of Jerusalem between 1840 and 1940 demonstrates the creation, completion, and 3D representation of the dataset, highlighting the versatility and effectiveness of our approach. This research contributes to the accessibility and accuracy of historical city models, providing tools for the generation of informative 3D models. By incorporating machine learning models and maintaining the dynamic nature of the models, we ensure the possibility of supporting ongoing updates and refinement based on newly acquired data. Our procedural modeling methodology offers a streamlined and open-source solution for historical city reconstruction, eliminating the need for additional software and increasing the usability and practicality of the process. Full article
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